Compositions and methods for diagnosing lung cancers using gene expression profiles

ABSTRACT

Methods and compositions are provided for diagnosing lung cancer in a mammalian subject by use of 10 or more selected genes, e.g., a gene expression profile, from the blood of the subject which is characteristic of disease. The gene expression profile includes 10 or more genes of Table I or Table II herein.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation application of U.S. application Ser. No. 16/312,036, filed Dec. 20, 2018, which is a National Stage Entry under 35 U.S.C. 371 of International Patent Application No. PCT/US2017/038571, filed Jun. 21, 2017, which claims priority to U.S. Provisional Application No. 62/352,865, filed Jun. 21, 2016. These applications are incorporated by reference herein.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under Grant No. CA010815 awarded by the National Institutes of Health. The government has certain rights in the invention.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED IN ELECTRONIC FORM

The contents of the electronic sequence listing (WST164USC1_SeqList.xml; size 536,359 bytes; and Date of Creation: Apr. 25, 2023) is herein incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

Lung cancer is the most common worldwide cause of cancer mortality. In the United States, lung cancer is the second most prevalent cancer in both men and women and will account for more than 174,000 new cases per year and more than 162,000 cancer deaths. In fact, lung cancer accounts for more deaths each year than from breast, prostate and colorectal cancers combined.

The high mortality (80-85% in five years), which has shown little or no improvement in the past 30 years, emphasizes the fact that new and effective tools to facilitate early diagnosis prior to metastasis to regional nodes or beyond the lung are needed.

High risk populations include smokers, former smokers, and individuals with markers associated with genetic predispositions. Because surgical removal of early stage tumors remains the most effective treatment for lung cancer, there has been great interest in screening high-risk patients with low dose spiral CT (LDCT). This strategy identifies non-calcified pulmonary nodules in approximately 30-70% of high risk individuals but only a small proportion of detected nodules are ultimately diagnosed as lung cancers (0.4 to 2.7%). Currently, the only way to differentiate subjects with lung nodules of benign etiology from subjects with malignant nodules is an invasive biopsy, surgery, or prolonged observation with repeated scanning. Even using the best clinical algorithms, 20-55% of patients selected to undergo surgical lung biopsy for indeterminate lung nodules, are found to have benign disease and those that do not undergo immediate biopsy or resection require sequential imaging studies. The use of serial CT in this group of patients runs the risk of delaying potential curable therapy, along with the costs of repeat scans, the not-insignificant radiation doses, and the anxiety of the patient.

Ideally, a diagnostic test would be easily accessible, inexpensive, demonstrate high sensitivity and specificity, and result in improved patient outcomes (medically and financially). Others have shown that classifiers which utilize epithelial cells have high accuracy. However, harvesting these cells requires an invasive bronchoscopy. See, Silvestri et al, N Engl J Med. 2015 Jul. 16; 373(3): 243-251, which is incorporated herein by reference.

Efforts are in progress to develop non-invasive diagnostics using sputum, blood or serum and analyzing for products of tumor cells, methylated tumor DNA, single nucleotide polymorphism (SNPs) expressed messenger RNA or proteins. This broad array of molecular tests with potential utility for early diagnosis of lung cancer has been discussed in the literature. Although each of these approaches has its own merits, none has yet passed the exploratory stage in the effort to detect patients with early stage lung cancer, even in high-risk groups, or patients which have a preliminary diagnosis based on radiological and other clinical factors. A simple blood test, a routine event associated with regular clinical office visits, would be an ideal diagnostic test.

SUMMARY OF THE INVENTION

In one aspect, a composition or kit for diagnosing or evaluating a lung cancer in a mammalian subject includes ten (10) or more polynucleotides or oligonucleotides, wherein each polynucleotide or oligonucleotide hybridizes to a different gene, gene fragment, gene transcript or expression product in a patient sample. Each gene, gene fragment, gene transcript or expression product is selected from the genes of Table I or Table II. In one embodiment, at least one polynucleotide or oligonucleotide is attached to a detectable label. In one embodiment, the composition or kit includes polynucleotides or oligonucleotides which detect the gene, gene fragment, gene transcript or expression product of each of the 559 genes in Table I. In another embodiment, the composition or kit includes polynucleotides or oligonucleotides which detect the gene, gene fragment, gene transcript or expression product of each of the 100 genes in Table II.

In another aspect, a composition or kit for diagnosing or evaluating a lung cancer in a mammalian subject includes ten (10) or more ligands, wherein each ligand hybridizes to a different gene expression product in a patient sample. Each gene expression product is selected from the genes of Table I or Table II. In one embodiment, at least one ligand is attached to a detectable label. In one embodiment, the composition or kit includes ligands which detect the expression products of each of the 559 genes in Table I. In another embodiment, the composition or kit includes ligands which detect the expression products of each of the 100 genes in Table II.

The compositions described herein enable detection of changes in expression in the genes in the subject's gene expression profile from that of a reference gene expression profile. The various reference gene expression profiles are described below. In one embodiment, the composition provides the ability to distinguish a cancerous tumor from a non-cancerous nodule.

In another aspect, a method for diagnosing or evaluating a lung cancer in a mammalian subject involves identifying changes in the expression of three or more genes in the sample of a subject, said genes selected from the genes of Table I or Table II, and comparing that subject's gene expression levels with the levels of the same genes in a reference or control, wherein changes in expression of said gene expression correlates with a diagnosis or evaluation of a lung cancer. In one embodiment, the changes in expression of said gene expression provides the ability to distinguish a cancerous tumor from a non-cancerous nodule.

In another aspect, a method for diagnosing or evaluating a lung cancer in a mammalian subject involves identifying a gene expression profile in the blood of a subject, the gene expression profile comprising 10 or more gene expression products of 10 or more informative genes as described herein. The 10 or more informative genes are selected from the genes of Table I or Table II. In one embodiment, the gene expression profile contains all 559 genes of Table I. In another embodiment, the gene expression profile contains all 100 genes of Table II. The subject's gene expression profile is compared with a reference gene expression profile from a variety of sources described below. Changes in expression of the informative genes correlate with a diagnosis or evaluation of a lung cancer. In one embodiment, the changes in expression of said gene expression provides the ability to distinguish a cancerous tumor from a non-cancerous nodule.

In another aspect, a method of detecting lung cancer in a patient is provided. The method includes obtaining a sample from the patient; and detecting a change in expression in at least 10 genes selected from Table I or Table II in the patient sample as compared to a control by contacting the sample with a composition comprising oligonucleotides, polynucleotides or ligands specific for each different gene transcript or expression product of the at least 10 gene of Table I or Table II and detecting binding between the oligonucleotide, polynucleotide or ligand and the gene product or expression product.

In yet another aspect, a method of diagnosing lung cancer in a subject is provided. The method includes obtaining a blood sample from a subject; detecting a change in expression in at least 10 genes selected from Table I or Table II in the patient sample as compared to a control by contacting the sample with a composition comprising oligonucleotides, polynucleotides or ligands specific for each different gene transcript or expression product of the at least 10 gene of Table I or Table II and detecting binding between the oligonucleotide, polynucleotide or ligand and the gene product or expression product; and diagnosing the subject with cancer when changes in expression of the subject's genes from those of the reference are detected.

In another aspect, a method of diagnosing and treating lung cancer in a subject having a neoplastic growth is provided. The method includes obtaining a blood sample from a subject; detecting a change in expression in at least 10 genes selected from Table I or Table II in the patient sample as compared to a control by contacting the sample with a composition comprising oligonucleotides, polynucleotides or ligands specific for each different gene transcript or expression product of the at least 10 gene of Table I or Table II and detecting binding between the oligonucleotide, polynucleotide or ligand and the gene product or expression product; diagnosing the subject with cancer when changes in expression of the subject's genes from those of the reference are detected; and removing the neoplastic growth. Other appropriate treatments may also be provided.

Other aspects and advantages of these compositions and methods are described further in the following detailed description of the preferred embodiments thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a table showing patient characteristics for the samples used in Example 1.

FIGS. 2A and 2B are graphs showing the cross validated support vector machine classifier (CV SVM) of all 610 samples (FIG. 2A, Accuracy=0.75, ROC Area=0.81. According to the curve, when the sensitivity is 0.91, the specificity is 0.46; when the sensitivity is 0.72, the specificity is 0.77) and a balanced set of 556 samples (FIG. 2B, Accuracy=0.76, ROC Area=0.81, According to the curve, when the sensitivity is 0.90, the specificity is 0.48; when the sensitivity is 0.76, the specificity is 0.77), using the 559 Classifier. The full and balanced sets show similar performance.

FIG. 3 is a bar graph showing sensitivity of the classifier by nodule size groups (x-axis). Data shows that larger nodules are more likely to be misclassified (p=1.54*10-4).

FIGS. 4A to 4C show the classification of samples groups (cancer, FIG. 4B, n=204; and nodule, FIG. 4C, n=331) stratified by lesion size. Over cancers >5 mm and higher, r=0.95. For nodules of all sizes, r=0.97. The chart (FIG. 4A) shows the sensitivity and specificity of the classification of cancers and nodules based on lesion size. These numbers are shown in bar graph form below.

FIGS. 5A and 5B are graphs showing the cross validated support vector machine classifier (CV SVM) of all cancer samples (n=278) vs. small nodules (<10 mm) (n=244) (FIG. 5A, Accuracy=0.79, ROC Area=0.85. According to the curve, when the sensitivity is 0.90, the specificity is 0.54; when the sensitivity is 0.77, the specificity is 0.82) and 10-fold CV SVM using all cancer samples (n=278) vs. large nodules (≥10 mm) (n=88) (FIG. 5B, Accuracy=0.76, ROC Area=0.71. According to the curve, when the sensitivity is 0.90, the specificity is 0.24; when the sensitivity is 0.87, the specificity is 0.42).

FIG. 6 is a graph showing the cross validated support vector machine classifier (CV SVM) of 25% of the data set used for the 559 Classifier, used as a testing set for the 100 Classifier. ROC Area=0.82. According to the curve, when the sensitivity is 0.90, the specificity is 0.62; when the sensitivity is 0.79, the specificity is 0.68; and when the sensitivity is 0.71, the specificity is 0.75.

DETAILED DESCRIPTION OF THE INVENTION

The methods and compositions described herein apply gene expression technology to blood screening for the detection and diagnosis of lung cancer. The compositions and methods described herein provide the ability to distinguish a cancerous tumor from a non-cancerous nodule, by determining a characteristic RNA expression profile of the genes of the blood of a mammalian, preferably human, subject. The profile is compared with the profile of one or more subjects of the same class (e.g., patients having lung cancer or a non-cancerous nodule) or a control to provide a useful diagnosis.

These methods of lung cancer screening employ compositions suitable for conducting a simple and cost-effective and non-invasive blood test using gene expression profiling that could alert the patient and physician to obtain further studies, such as a chest radiograph or CT scan, in much the same way that the prostate specific antigen is used to help diagnose and follow the progress of prostate cancer. The application of these profiles provides overlapping and confirmatory diagnoses of the type of lung disease, beginning with the initial test for malignant vs. non-malignant disease.

“Patient” or “subject” as used herein means a mammalian animal, including a human, a veterinary or farm animal, a domestic animal or pet, and animals normally used for clinical research. In one embodiment, the subject of these methods and compositions is a human.

“Control” or “Control subject” as used herein refers to the source of the reference gene expression profiles as well as the particular panel of control subjects described herein. In one embodiment, the control or reference level is from a single subject. In another embodiment, the control or reference level is from a population of individuals sharing a specific characteristic. In yet another embodiment, the control or reference level is an assigned value which correlates with the level of a specific control individual or population, although not necessarily measured at the time of assaying the test subject's sample. In one embodiment, the control subject or reference is from a patient (or population) having a non-cancerous nodule. In another embodiment, the control subject or reference is from a patient (or population) having a cancerous tumor. In other embodiments, the control subject can be a subject or population with lung cancer, such as a subject who is a current or former smoker with malignant disease, a subject with a solid lung tumor prior to surgery for removal of same; a subject with a solid lung tumor following surgical removal of said tumor; a subject with a solid lung tumor prior to therapy for same; and a subject with a solid lung tumor during or following therapy for same. In other embodiments, the controls for purposes of the compositions and methods described herein include any of the following classes of reference human subject with no lung cancer. Such non-healthy controls (NHC) include the classes of smoker with non-malignant disease, a former smoker with non-malignant disease (including patients with lung nodules), a non-smoker who has chronic obstructive pulmonary disease (COPD), and a former smoker with COPD. In still other embodiments, the control subject is a healthy non-smoker with no disease or a healthy smoker with no disease.

“Sample” as used herein means any biological fluid or tissue that contains immune cells and/or cancer cells. The most suitable sample for use in this invention includes whole blood. Other useful biological samples include, without limitation, peripheral blood mononuclear cells, plasma, saliva, urine, synovial fluid, bone marrow, cerebrospinal fluid, vaginal mucus, cervical mucus, nasal secretions, sputum, semen, amniotic fluid, bronchoscopy sample, bronchoalveolar lavage fluid, and other cellular exudates from a patient having cancer. Such samples may further be diluted with saline, buffer or a physiologically acceptable diluent. Alternatively, such samples are concentrated by conventional means.

As used herein, the term “cancer” refers to or describes the physiological condition in mammals that is typically characterized by unregulated cell growth. More specifically, as used herein, the term “cancer” means any lung cancer. In one embodiment, the lung cancer is non-small cell lung cancer (NSCLC). In a more specific embodiment, the lung cancer is lung adenocarcinoma (AC or LAC). In another more specific embodiment, the lung cancer is lung squamous cell carcinoma (SCC or LSCC). In another embodiment, the lung cancer is a stage I or stage II NSCLC. In still another embodiment, the lung cancer is a mixture of early and late stages and types of NSCLC.

The term “tumor,” as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. The term “nodule” refers to an abnormal buildup of tissue which is benign. The term “cancerous tumor” refers to a malignant tumor.

By “diagnosis” or “evaluation” it is meant a diagnosis of a lung cancer, a diagnosis of a stage of lung cancer, a diagnosis of a type or classification of a lung cancer, a diagnosis or detection of a recurrence of a lung cancer, a diagnosis or detection of a regression of a lung cancer, a prognosis of a lung cancer, or an evaluation of the response of a lung cancer to a surgical or non-surgical therapy. In one embodiment, “diagnosis” or “evaluation” refers to distinguishing between a cancerous tumor and a benign pulmonary nodule.

As used herein, “sensitivity” (also called the true positive rate), measures the proportion of positives that are correctly identified as such (e.g., the percentage of sick people who are correctly identified as having the condition).

As used herein, “specificity” (also called the true negative rate) measures the proportion of negatives that are correctly identified as such (e.g., the percentage of healthy people who are correctly identified as not having the condition).

By “change in expression” is meant an upregulation of one or more selected genes in comparison to the reference or control; a downregulation of one or more selected genes in comparison to the reference or control; or a combination of certain upregulated genes and down regulated genes.

By “therapeutic reagent” or “regimen” is meant any type of treatment employed in the treatment of cancers with or without solid tumors, including, without limitation, chemotherapeutic pharmaceuticals, biological response modifiers, radiation, diet, vitamin therapy, hormone therapies, gene therapy, surgical resection, etc.

By “informative genes” as used herein is meant those genes the expression of which changes (either in an up-regulated or down-regulated manner) characteristically in the presence of lung cancer. A statistically significant number of such informative genes thus form suitable gene expression profiles for use in the methods and compositions. Such genes are shown in Table I and Table II below. Such genes make up the “expression profile”.

The term “statistically significant number of genes” in the context of this invention differs depending on the degree of change in gene expression observed. The degree of change in gene expression varies with the type of cancer and with the size or spread of the cancer or solid tumor. The degree of change also varies with the immune response of the individual and is subject to variation with each individual. For example, in one embodiment of this invention, a large change, e.g., 2-3 fold increase or decrease in a small number of genes, e.g., in about 10 to 20 genes, is statistically significant. In another embodiment, a smaller relative change in about 15 more genes is statistically significant.

Thus, the methods and compositions described herein contemplate examination of the expression profile of a “statistically significant number of genes” ranging from 5 to about 559 genes in a single profile. In one embodiment, the genes are selected from Table I. In another embodiment, the genes are selected from Table II. In one embodiment, the gene profile is formed by a statistically significant number of 5 or more genes. In one embodiment, the gene profile is formed by a statistically significant number of 10 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 15 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 20 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 25 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 30 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 35 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 40 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 45 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 50 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 60 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 65 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 70 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 75 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 80 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 85 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 90 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 95 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 100 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 200 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 300 or more genes. In another embodiment, the gene profile is formed by a statistically significant number of 350 or more genes. In still another embodiment, the gene profile is formed by 400 or more genes. In still another embodiment, the gene profile is formed by 539 genes. In still another embodiment, the gene profile is formed by 559 genes. In still other embodiments, the gene profiles examined as part of these methods contain, as statistically significant numbers of genes, from 10 to 559 genes, and any numbers therebetween. In another embodiment, the gene profile is formed by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, or all 559 genes of Table I. In another embodiment, the gene profile is formed by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or all 100 genes of Table II.

Table I and Table II below refer to a collection of known genes useful in discriminating between a subject having a lung cancer, e.g., NSCLC, and subjects having benign (non-malignant) lung nodules. The sequences of the genes identified in Table I and Table II are publicly available. One skilled in the art may readily reproduce the compositions and methods described herein by use of the sequences of the genes, all of which are publicly available from conventional sources, such as GenBank. The GenBank accession number for each gene is provided.

The term “microarray” refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide or oligonucleotide probes, on a substrate.

The term “polynucleotide,” when used in singular or plural form, generally refers to any polyribonucleotide or polydeoxribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides as defined herein include, without limitation, single- and double-stranded DNA, DNA including single- and double-stranded regions, single- and double-stranded RNA, and RNA including single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or include single- and double-stranded regions. In addition, the term “polynucleotide” as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an oligonucleotide. The term “polynucleotide” specifically includes cDNAs. The term includes DNAs (including cDNAs) and RNAs that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons are “polynucleotides” as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term “polynucleotides” as defined herein. In general, the term “polynucleotide” embraces all chemically, enzymatically and/or metabolically modified forms of unmodified polynucleotides, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including simple and complex cells.

The term “oligonucleotide” refers to a relatively short polynucleotide, including, without limitation, single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNA:DNA hybrids and double-stranded DNAs. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by chemical methods, for example using automated oligonucleotide synthesizers that are commercially available. However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA-mediated techniques and by expression of DNAs in cells and organisms.

The terms “differentially expressed gene”, “differential gene expression” and their synonyms, which are used interchangeably, refer to a gene whose expression is activated to a higher or lower level in a subject suffering from a disease, specifically cancer, such as lung cancer, relative to its expression in a control subject, such as a subject having a benign nodule. The terms also include genes whose expression is activated to a higher or lower level at different stages of the same disease. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product. Such differences may be evidenced by a change in mRNA levels, surface expression, secretion or other partitioning of a polypeptide, for example. Differential gene expression may include a comparison of expression between two or more genes or their gene products, or a comparison of the ratios of the expression between two or more genes or their gene products, or even a comparison of two differently processed products of the same gene, which differ between normal subjects, non-health controls and subjects suffering from a disease, specifically cancer, or between various stages of the same disease. Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products among, for example, normal and diseased cells, or among cells which have undergone different disease events or disease stages. For the purpose of this invention, “differential gene expression” is considered to be present when there is a statistically significant (p<0.05) difference in gene expression between the subject and control samples.

The term “over-expression” with regard to an RNA transcript is used to refer to the level of the transcript determined by normalization to the level of reference mRNAs, which might be all measured transcripts in the specimen or a particular reference set of mRNAs.

The phrase “gene amplification” refers to a process by which multiple copies of a gene or gene fragment are formed in a particular cell or cell line. The duplicated region (a stretch of amplified DNA) is often referred to as “amplicon.” Usually, the amount of the messenger RNA (mRNA) produced, i.e., the level of gene expression, also increases in the proportion of the number of copies made of the particular gene expressed.

In the context of the compositions and methods described herein, reference to “10 or more”, “at least 10” etc. of the genes listed in Table I or Table II means any one or any and all combinations of the genes listed. For example, suitable gene expression profiles include profiles containing any number between at least 5 through 559 genes from Table I. In another example, suitable gene expression profiles include profiles containing any number between at least 5 through 100 genes from Table II. In one embodiment, gene profiles formed by genes selected from a table are used in rank order, e.g., genes ranked in the top of the list demonstrated more significant discriminatory results in the tests, and thus may be more significant in a profile than lower ranked genes. However, in other embodiments the genes forming a useful gene profile do not have to be in rank order and may be any gene from the table. As used herein, the term “100 Classifier” or “100 Biomarker Classifier” refers to the 100 genes of Table II. As used herein, the term “559 Classifier” or “559 Biomarker Classifier” refers to the 559 genes of Table I. However, subsets of the genes of Table I or Table II, as described herein, are also useful, and, in another embodiment, the terms may refer to those subsets as well.

As used herein, “labels” or “reporter molecules” are chemical or biochemical moieties useful for labeling a nucleic acid (including a single nucleotide), polynucleotide, oligonucleotide, or protein ligand, e.g., amino acid or antibody. “Labels” and “reporter molecules” include fluorescent agents, chemiluminescent agents, chromogenic agents, quenching agents, radionucleotides, enzymes, substrates, cofactors, inhibitors, magnetic particles, and other moieties known in the art. “Labels” or “reporter molecules” are capable of generating a measurable signal and may be covalently or noncovalently joined or bound to an oligonucleotide or nucleotide (e.g., a non-natural nucleotide) or ligand.

Unless defined otherwise in this specification, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs and by reference to published texts, which provide one skilled in the art with a general guide to many of the terms used in the present application.

I. GENE EXPRESSION PROFILES

The inventors have shown that the gene expression profiles of the whole blood of lung cancer patients differ significantly from those seen in patients having non-cancerous lung nodules. For example, changes in the gene expression products of the genes of Table I and/or Table II can be observed and detected by the methods of this invention in the normal circulating blood of patients with early stage solid lung tumors.

The gene expression profiles described herein provide new diagnostic markers for the early detection of lung cancer and could prevent patients from undergoing unnecessary procedures relating to surgery or biopsy for a benign nodule. Since the risks are very low, the benefit to risk ratio is very high. In one embodiment, the methods and compositions described herein may be used in conjunction with clinical risk factors to help physicians make more accurate decisions about how to manage patients with lung nodules. Another advantage of this invention is that diagnosis may occur early since diagnosis is not dependent upon detecting circulating tumor cells which are present in only vanishing small numbers in early stage lung cancers.

In one aspect, a composition is provided for classifying a nodule as cancerous or benign in a mammalian subject. In one embodiment, the composition includes at least 10 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In another embodiment, the composition includes at least 100 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the polynucleotide or oligonucleotide or ligand hybridizes to an mRNA.

TABLE I Rank Sequence ID# Gene Class Name 1 PLEKHG4 NM_015432.3 Endogenous 2 SLC25A20 NM_000387.5 Endogenous 3 LETM2 NM_144652.3 Endogenous 4 GLIS3 NM_001042413.1 Endogenous 5 LOC100132797 XR_036994.1 Endogenous 6 ARHGEF5 NM_005435.3 Endogenous 7 TCF7L2 NM_030756.4 Endogenous 8 SFRS2IP NM_004719.2 Endogenous 9 CFD NM_001928.2 Endogenous 10 AZI2 NM_022461.4 Endogenous 11 STOM NM_004099.5 Endogenous 12 CD1A NM_001763.2 Endogenous 13 PANK2 NM_153640.2 Endogenous 14 CNIH4 NM_014184.3 Endogenous 15 EVI2A NM_014210.3 Endogenous 16 BATF NM_006399.3 Endogenous 17 TCP1 NM_030752.2 Endogenous 18 BX108566 BX108566.1 Endogenous 19 ANXA1 NM_000700.2 Endogenous 20 PSMA3 NM_152132.2 Endogenous 21 IRF4 NM_002460.1 Endogenous 22 STAG3 NM_012447.3 Endogenous 23 NDUFS4 NM_002495.2 Endogenous 24 HAT1 NM_003642.3 Endogenous 25 ANXA1 b NM_000700.1 Endogenous 26 LOC148137 NM_144692.1 Endogenous 27 LDHA NM_001165416.1 Endogenous 28 PSME3 NM_005789.3 Endogenous 29 REPS1 NM_001128617.2 Endogenous 30 CDH5 NM_001795.3 Endogenous 31 NAT5 NM_181528.3 Endogenous 32 PLAC8 NM_001130715.1 Endogenous 33 GSTO1 NM_004832.2 Endogenous 34 DGUOK NM_080916.2 Endogenous 35 OLR1 NM_002543.3 Endogenous 36 MYST4 NM_012330.3 Endogenous 37 TIMM8B ENST00000504148.1 Endogenous 38 LY96 NM_015364.4 Endogenous 39 CCDC72 NM_015933.4 Endogenous 40 ATP5I NM_007100.2 Endogenous 41 WDR91 NM_014149.3 Endogenous 42 MAGEA3 NM_005362.3 Endogenous 43 AK093878 AK093878.1 Endogenous 44 EYA3 NM_001990.3 Endogenous 45 ACAA2 NM_006111.2 Endogenous 46 ETFDH NM_004453.3 Endogenous 47 CCT6A NM_001762.3 Endogenous 48 HSCB NM_172002.3 Endogenous 49 EMR4 NM_001080498.2 Endogenous 50 USP5 NM_003481.2 Endogenous 51 SIK1 NM_173354.3 Endogenous 52 SYNJ1 NM_003895.3 Endogenous 53 KLRB1 NM_002258.2 Endogenous 54 CLK2 XM_941392.1 Endogenous 55 SNORA56 NR_002984.1 Endogenous 56 TP53BP1 NM_005657.2 Endogenous 57 RBX1 NM_014248.3 Endogenous 58 CNPY2 NM_014255.5 Endogenous 59 RELA NM_021975.2 Endogenous 60 LOC732371 XM_001133019.1 Endogenous 61 TMEM218 NM_001080546.2 Endogenous 62 LOC91431 NM_001099776.1 Endogenous 63 GZMB NM_004131.3 Endogenous 64 CAMP NM_004345.4 Endogenous 65 RBM16 NM_014892.4 Endogenous 66 MID1IP1 NM_021242.5 Endogenous 67 LOC399942 XM_934471.1 Endogenous 68 COMMD6 NM_203497.3 Endogenous 69 PPP6C NM_002721.4 Endogenous 70 BCOR NM_017745.5 Endogenous 71 PDCD10 NM_145859.1 Endogenous 72 HLA-DMB NM_002118.3 Endogenous 73 DNAJB1 NM_006145.2 Endogenous 74 KYNU NM_001032998.1 Endogenous 75 TM2D2 NM_078473.2 Endogenous 76 FAM179A NM_199280.2 Endogenous 77 FAM43A NM_153690.4 Endogenous 78 QTRTD1 NM_024638.3 Endogenous 79 MARCKSL1 NM_023009.5 Endogenous 80 FAM193A NM_003704.3 Endogenous 81 AK026725 AK026725.1 Endogenous 82 SERPINB10 NM_005024.1 Endogenous 83 OSBP ILMN_1706376.1 Endogenous 84 ST6GAL1 NM_003032.2 Endogenous 85 NDUFAF2 NM_174889.4 Endogenous 86 UBE2I NM_194259.2 Endogenous 87 CTAG1B NM_001327.2 Endogenous 88 TRAF6 NM_145803.1 Endogenous 89 REPIN1 NM_014374.3 Endogenous 90 LAMA5 NM_005560.4 Endogenous 91 TBC1D12 NM_015188.1 Endogenous 92 TGIF1 b NM_173208.1 Endogenous 93 LOC728533 XR_015610.3 Endogenous 94 CLN8 NM_018941.3 Endogenous 95 COX7B NM_001866.2 Endogenous 96 DYNC2LI1 NM_016008.3 Endogenous 97 ANP32B NM_006401.2 Endogenous 98 PTGDR2 NM_004778.1 Endogenous 99 MRPS16 NM_016065.3 Endogenous 100 NIPBL NM_133433.3 Endogenous 101 PPP2R5C NM_178588.1 Endogenous 102 DPF2 NM_006268.4 Endogenous 103 RAB10 NM_016131.4 Endogenous 104 MYADM NM_001020820.1 Endogenous 105 CCND3 NM_001760.2 Endogenous 106 CC2D1B NM_032449.2 Endogenous 107 HLA-G NM_002127.4 Endogenous 108 CKS2 NM_001827.1 Endogenous 109 HPSE NM_006665.5 Endogenous 110 UBE2G1 NM_003342.4 Endogenous 111 MED16 NM_005481.2 Endogenous 112 LOC339674 XM_934917.1 Endogenous 113 RNF114 NM_018683.3 Endogenous 114 KIR2DS3 NM_012313.1 Endogenous 115 AMD1 NM_001634.4 Endogenous 116 S100A8 NM_002964.4 Endogenous 117 NFATC4 NM_001136022.2 Endogenous 118 RPL39L NM_052969.1 Endogenous 119 LOC399753 XM_930634.1 Endogenous 120 FKBP1A NM_054014.3 Endogenous 121 CHMP5 NM_016410.5 Endogenous 122 CABC1 NM_020247.4 Endogenous 123 HLA-B NM_005514.6 Endogenous 124 TRIM39 NM_021253.3 Endogenous 125 LOC645914 XM_928884.1 Endogenous 126 CD79A NM_021601.3 Endogenous 127 GLRX ILMN_1737308.1 Endogenous 128 RPL26L1 NM_016093.2 Endogenous 129 USP21 NM_012475.4 Endogenous 130 CD70 NM_001252.2 Endogenous 131 SPINK5 NM_006846.3 Endogenous 132 HUWE1 NM_031407.6 Endogenous 133 STK38 NM_007271.3 Endogenous 134 SEMG1 NM_003007.2 Endogenous 135 NDUFA4 NM_002489.3 Endogenous 136 MYADM b NM_001020820.1 Endogenous 137 SGK1 b NM_005627.3 Endogenous 138 SLAMF8 NM_020125.2 Endogenous 139 LOC653773 XM_938755.1 Endogenous 140 RPS24 NM_001026.4 Endogenous 141 LOC338799 NR_002809.2 Endogenous 142 MAP3K7 NM_145333.1 Endogenous 143 KLRD1 NM_002262.3 Endogenous 144 LOC732111 XM_001134275.1 Endogenous 145 CD69 NM_001781.2 Endogenous 146 DDIT4 NM_019058.2 Endogenous 147 C1orf222 NM_001003808.1 Endogenous 148 PFAS NM_012393.2 Endogenous 149 USP9Y NM_004654.3 Endogenous 150 COLEC12 NM_130386.2 Endogenous 151 VPS37C NM_017966.4 Endogenous 152 SAP130 NM_024545.3 Endogenous 153 CDC42EP2 NM_006779.3 Endogenous 154 LOC643319 XM_927980.1 Endogenous 155 ASF1B NM_018154.2 Endogenous 156 AK094576 AK094576.1 Endogenous 157 BANP NM_079837.2 Endogenous 158 TBK1 NM_013254.2 Endogenous 159 GNS NM_002076.3 Endogenous 160 IL1R2 NM_173343.1 Endogenous 161 CLEC4C NM_203503.1 Endogenous 162 TM9SF1 NM_006405.6 Endogenous 163 PTGDR NM_000953.2 Endogenous 164 GOLGA3 NM_005895.3 Endogenous 165 CLEC4A NM_194448.2 Endogenous 166 TSC1 NM_000368.4 Endogenous 167 SFMBT1 NM_001005158.2 Endogenous 168 GLT25D1 NM_024656.2 Endogenous 169 LOC100130229 XM_001717158.1 Endogenous 170 PHF8 NM_015107.2 Endogenous 171 PUM1 NM_001020658.1 Endogenous 172 SMARCC1 NM_003074.3 Endogenous 173 AK126342 AK126342.1 Endogenous 174 ACSL5 NM_203379.1 Endogenous 175 TGIF1 NM_003244.2 Endogenous 176 BF375676 BF375676.1 Endogenous 177 SPA17 NM_017425.3 Endogenous 178 FLNB NM_001457.3 Endogenous 179 FAM105B NM_138348.4 Endogenous 180 CPPED1 NM_018340.2 Endogenous 181 TRIM32 NM_012210.3 Endogenous 182 RNF34 NM_025126.3 Endogenous 183 SLC45A3 NM_033102.2 Endogenous 184 P2RY10 NM_198333.1 Endogenous 185 AKR1C3 NM_003739.4 Endogenous 186 NME1-NME2 NM_001018136.2 Endogenous 187 AMPD3 NM_000480.2 Endogenous 188 HSP90AB1 NM_007355.3 Endogenous 189 RBM4B NM_031492.3 Endogenous 190 DMBT1 NM_007329.2 Endogenous 191 TMCO1 NM_019026.3 Endogenous 192 CASP2 NM_032983.3 Endogenous 193 C1orf103 NM_018372.3 Endogenous 194 ARHGAP17 NM_018054.5 Endogenous 195 IFNA17 NM_021268.2 Endogenous 196 CTSZ NM_001336.3 Endogenous 197 DBI NM_001079862.1 Endogenous 198 TXNRD1 b NM_182743.2 Endogenous 199 KIAA0460 NM_015203.4 Endogenous 200 PDGFD NM_033135.3 Endogenous 201 ATG5 NM_004849.2 Endogenous 202 ITFG2 NM_018463.3 Endogenous 203 HERC1 NM_003922.3 Endogenous 204 MEN1 NM_130799.2 Endogenous 205 IFI27L2 NM_032036.2 Endogenous 206 LOC729887 XR_040891.2 Endogenous 207 PI4K2A NM_018425.3 Endogenous 208 RAG1 NM_000448.2 Endogenous 209 CREB5 NM_182898.3 Endogenous 210 SLC6A12 NM_003044.4 Endogenous 211 CDKN1A NM_000389.2 Endogenous 212 AW173314 AW173314.1 Endogenous 213 SAP130 b NM_024545.3 Endogenous 214 ABCA5 NM_018672.4 Endogenous 215 SLC25A37 NM_016612.2 Endogenous 216 MYLIP NM_013262.3 Endogenous 217 GATA2 NM_001145662.1 Endogenous 218 ATP5L NM_006476.4 Endogenous 219 RPS27L NM_015920.3 Endogenous 220 DB338252 DB338252.1 Endogenous 221 FRAT2 NM_012083.2 Endogenous 222 CCL4 NM_002984.2 Endogenous 223 CD79B NM_000626.2 Endogenous 224 MBD1 NM_015844.2 Endogenous 225 TIAM1 NM_003253.2 Endogenous 226 HSD11B1 NM_181755.1 Endogenous 227 TPR NM_003292.2 Endogenous 228 EID2B NM_152361.2 Endogenous 229 PDSS1 NM_014317.3 Endogenous 230 C9orf164 NM_182635.1 Endogenous 231 ARHGEF18 NM_015318.3 Endogenous 232 TXNRD1 NM_001093771.2 Endogenous 233 HNRNPAB NM_004499.3 Endogenous 234 TTN NM_133378.4 Endogenous 235 EP300 NM_001429.2 Endogenous 236 CCDC97 NM_052848.1 Endogenous 237 HK3 NM_002115.2 Endogenous 238 CRKL NM_005207.3 Endogenous 239 NCOA5 NM_020967.2 Endogenous 240 AK124143 AK124143.1 Endogenous 241 LBA1 NM_014831.2 Endogenous 242 SLC9A3R1 NM_004252.3 Endogenous 243 CRY2 NM_021117.3 Endogenous 244 ATG4B NM_178326.2 Endogenous 245 CD97 NM_078481.3 Endogenous 246 TTC9 NM_015351.1 Endogenous 247 BMPR2 NM_001204.6 Endogenous 248 LPIN2 NM_014646.2 Endogenous 249 UBA1 NM_003334.3 Endogenous 250 SETD1B XM_037523.11 Endogenous 251 PRPF8 NM_006445.3 Endogenous 252 RNASE2 NM_002934.2 Endogenous 253 KIAA0101 NM_014736.4 Endogenous 254 ARG1 NM_000045.3 Endogenous 255 UBTF NM_001076683.1 Endogenous 256 MFSD1 NM_022736.2 Endogenous 257 IDO1 NM_002164.3 Endogenous 258 MS4A6A NM_022349.3 Endogenous 259 C22orf30 NM_173566.2 Endogenous 260 HNRNPK NM_031263.2 Endogenous 261 ARL8B NM_018184.2 Endogenous 262 SETD2 NM_014159.6 Endogenous 263 NCAPG NM_022346.4 Endogenous 264 EEF1B2 NM_001037663.1 Endogenous 265 TRIM39 b NM_172016.2 Endogenous 266 EHD4 NM_139265.3 Endogenous 267 IRF1 NM_002198.1 Endogenous 268 LOC100129022 XM_001716591.1 Endogenous 269 TRAF3IP2 NM_147686.3 Endogenous 270 PSMA6 NM_002791.2 Endogenous 271 RHOG NM_001665.3 Endogenous 272 CN312986 CN312986.1 Endogenous 273 PSMB8 NM_004159.4 Endogenous 274 ZNF239 NM_001099283.1 Endogenous 275 CLPTM1 NM_001294.3 Endogenous 276 NADK NM_023018.4 Endogenous 277 C8orf76 NM_032847.2 Endogenous 278 LIF NM_002309.3 Endogenous 279 EGR1 NM_001964.2 Endogenous 280 ARG1 b NM_000045.2 Endogenous 281 MERTK NM_006343.2 Endogenous 282 RHOU NM_021205.5 Endogenous 283 PFDN5 b NM_145897.2 Endogenous 284 MAGEA1 NM_004988.4 Endogenous 285 SEC24C NM_198597.2 Endogenous 286 SLC11A1 NM_000578.3 Endogenous 287 TCF20 NM_181492.2 Endogenous 288 AHCYL1 NM_001242676.1 Endogenous 289 TPT1 NM_003295.3 Endogenous 290 KIR2DL5A XM_001126354.1 Endogenous 291 IRAK2 NM_001570.3 Endogenous 292 C17orf51 XM_944416.1 Endogenous 293 C14orf156 NM_031210.5 Endogenous 294 ATP2C1 NM_014382.3 Endogenous 295 SOCS1 NM_003745.1 Endogenous 296 JAK1 NM_002227.1 Endogenous 297 RSL24D1 NM_016304.2 Endogenous 298 AP2S1 NM_021575.3 Endogenous 299 PHRF1 NM_020901.3 Endogenous 300 GPI NM_000175.2 Endogenous 301 NCR1 NM_004829.5 Endogenous 302 AKAP4 NM_139289.1 Endogenous 303 CD160 NM_007053.3 Endogenous 304 DDX23 NM_004818.2 Endogenous 305 GNL3 NM_014366.4 Endogenous 306 NFKB2 NM_002502.2 Endogenous 307 CSK NM_004383.2 Endogenous 308 PELP1 NM_014389.2 Endogenous 309 KLRF1 b NM_016523.2 Endogenous 310 CS NM_004077.2 Endogenous 311 PHCA NM_018367.6 Endogenous 312 LOC644315 XR_017529.2 Endogenous 313 NUDT18 NM_024815.3 Endogenous 314 XCL2 NM_003175.3 Endogenous 315 KLRC1 NM_002259.3 Endogenous 316 ARHGAP18 NM_033515.2 Endogenous 317 CTDSP2 NM_005730.3 Endogenous 318 P2RY5 NM_005767.5 Endogenous 319 CREB1 NM_004379.3 Endogenous 320 RHOB NM_004040.3 Endogenous 321 DCAF7 NM_005828.4 Endogenous 322 NUP153 NM_005124.3 Endogenous 323 AFTPH NM_017657.4 Endogenous 324 EWSR1 NM_005243.3 Endogenous 325 LYN NM_002350.1 Endogenous 326 CYBB NM_000397.3 Endogenous 327 TMEM70 NM_017866.5 Endogenous 328 PPP1R3E XM_927029.1 Endogenous 329 PSMB1 NM_002793.3 Endogenous 330 RERE b NM_012102.3 Endogenous 331 RXRA NM_002957.5 Endogenous 332 GZMA NM_006144.3 Endogenous 333 ERLIN1 NM_006459.3 Endogenous 334 KRTAP10-3 NM_198696.2 Endogenous 335 SAMSN1 NM_022136.3 Endogenous 336 LRRC47 NM_020710.2 Endogenous 337 MARCKS NM_002356.6 Endogenous 338 HOPX NM_139211.4 Endogenous 339 KLRF1 NM_016523.1 Endogenous 340 NFAT5 NM_138713.3 Endogenous 341 SLC15A2 NM_021082.3 Endogenous 342 STK16 NM_003691.2 Endogenous 343 KIR_Activating_Subgroup_2 NM_014512.1 Endogenous 344 TBCE NM_001079515.2 Endogenous 345 BAG3 NM_004281.3 Endogenous 346 SFRS4 NM_005626.4 Endogenous 347 AW270402 AW270402.1 Endogenous 348 CCL3L1 NM_021006.4 Endogenous 349 HERC3 NM_014606.2 Endogenous 350 RPL34 NM_000995.3 Endogenous 351 ALAS1 NM_000688.4 Endogenous 352 CCR9 NM_031200.1 Endogenous 353 CORO1C ILMN_1745954.1 Endogenous 354 FAIM3 NM_005449.4 Endogenous 355 SFPQ NM_005066.2 Endogenous 356 HOOK3 NM_032410.3 Endogenous 357 CD36 NM_000072.3 Endogenous 358 IL7 NM_000880.2 Endogenous 359 CBLL1 NM_024814.3 Endogenous 360 HVCN1 NM_032369.3 Endogenous 361 HMGB1 NM_002128.4 Endogenous 362 SIN3A NM_015477.2 Endogenous 363 CASP3 NM_032991.2 Endogenous 364 BQ189294 BQ189294.1 Endogenous 365 NDRG2 NM_016250.2 Endogenous 366 BX400436 BX400436.2 Endogenous 367 IFNAR2 NM_000874.3 Endogenous 368 MS4A6A b NM_152851.2 Endogenous 369 KLRC2 NM_002260.3 Endogenous 370 S100A12 b NM_005621.1 Endogenous 371 ATM NM_000051.3 Endogenous 372 NLRP3 NM_001079821.2 Endogenous 373 HAVCR2 NM_032782.3 Endogenous 374 C4B NM_001002029.3 Endogenous 375 CTSW NM_001335.3 Endogenous 376 TMEM170B NM_001100829.2 Endogenous 377 EIF4ENIF1 NM_019843.2 Endogenous 378 CCL3 NM_002983.2 Endogenous 379 CHCHD3 NM_017812.2 Endogenous 380 CST7 NM_003650.3 Endogenous 381 SFRS15 NM_020706.2 Endogenous 382 STIP1 NM_006819.2 Endogenous 383 MPDU1 NM_004870.3 Endogenous 384 DHX16 b NM_001164239.1 Endogenous 385 INTS4 NM_033547.3 Endogenous 386 USP16 NM_001032410.1 Endogenous 387 IFNAR1 NM_000629.2 Endogenous 388 ITCH NM_001257138.1 Endogenous 389 FOXK2 NM_004514.3 Endogenous 390 LOC642812 XR_036892.1 Endogenous 391 KIAA1967 NM_021174.5 Endogenous 392 LOC440928 XM_942885.1 Endogenous 393 NDUFV2 NM_021074.4 Endogenous 394 IL4 NM_000589.2 Endogenous 395 CIAPIN1 NM_020313.3 Endogenous 396 CXCL2 NM_002089.3 Endogenous 397 TXN NM_003329.3 Endogenous 398 PRG2 NM_002728.4 Endogenous 399 MS4A2 NM_000139.3 Endogenous 400 YPEL1 NM_013313.4 Endogenous 401 POLR2A NM_000937.4 Endogenous 402 C19orf10 NM_019107.3 Endogenous 403 IGFBP7 NM_001553.2 Endogenous 404 ITGAE NM_002208.4 Endogenous 405 CXCR5 b NM_001716.3 Endogenous 406 BID NM_001196.2 Endogenous 407 LOC100133273 XR_039238.1 Endogenous 408 FNBP1 NM_015033.2 Endogenous 409 IFNGR1 NM_000416.1 Endogenous 410 STAT6 NM_003153.4 Endogenous 411 CR2 NM_001006658.2 Endogenous 412 CCL3L3 NM_001001437.3 Endogenous 413 RFWD2 NM_022457.6 Endogenous 414 SP2 NM_003110.5 Endogenous 415 BAT2D1 NM_015172.3 Endogenous 416 CX3CL1 NM_002996.3 Endogenous 417 GPATCH3 NM_022078.2 Endogenous 418 CASP1 NM_033294.3 Endogenous 419 NAGK NM_017567.4 Endogenous 420 IER5 NM_016545.4 Endogenous 421 PHLPP2 NM_015020.3 Endogenous 422 RPL31 NM_000993.4 Endogenous 423 SPEN NM_015001.2 Endogenous 424 TMSB4X NM_021109.3 Endogenous 425 IL8RB NM_001557.3 Endogenous 426 XPC NR_027299.1 Endogenous 427 SNX11 NM_152244.1 Endogenous 428 SPN NM_003123.3 Endogenous 429 ANKHD1 NM_017747.2 Endogenous 430 CCR6 NM_031409.2 Endogenous 431 DZIP3 NM_014648.3 Endogenous 432 MRPL27 NM_148571.1 Endogenous 433 SREBF1 NM_001005291.2 Endogenous 434 CD14 NM_000591.2 Endogenous 435 TNFSF8 NM_001244.3 Endogenous 436 C3 NM_000064.2 Endogenous 437 FAM50B NM_012135.1 Endogenous 438 RASSF5 NM_182664.2 Endogenous 439 BU743228 BU743228.1 Endogenous 440 NFATC1 NM_172389.1 Endogenous 441 DOCK5 NM_024940.6 Endogenous 442 PACS1 NM_018026.3 Endogenous 443 CYP1B1 NM_000104.3 Endogenous 444 CLIC3 ILMN_1796423.1 Endogenous 445 PSMA4 NM_002789.3 Endogenous 446 ZNF341 NM_032819.4 Endogenous 447 PRPF3 NM_004698.2 Endogenous 448 PSMA6 b NM_002791.2 Endogenous 449 LOC648927 XR_038906.2 Endogenous 450 KCTD12 NM_138444.3 Endogenous 451 LOC440389 XM_498648.3 Endogenous 452 U2AF2 NM_007279.2 Endogenous 453 CLEC5A NM_013252.2 Endogenous 454 PRRG4 NM_024081.5 Endogenous 455 TNFRSF9 NM_001561.5 Endogenous 456 NDUFB3 NM_002491.2 Endogenous 457 BCL6 NM_001130845.1 Endogenous 458 SGK1 NM_005627.3 Endogenous 459 CIP29 NM_033082.3 Endogenous 460 CD160 b NM_007053.2 Endogenous 461 ARCN1 NM_001655.4 Endogenous 462 LOC151162 NR_024275.1 Endogenous 463 GPR65 NM_003608.3 Endogenous 464 CCR1 NM_001295.2 Endogenous 465 TFCP2 NM_005653.4 Endogenous 466 SGK NM_005627.3 Endogenous 467 RNF214 NM_207343.3 Endogenous 468 TMC8 NM_152468.4 Endogenous 469 RBM14 NM_006328.3 Endogenous 470 USP34 NM_014709.3 Endogenous 471 BACH2 NM_021813.3 Endogenous 472 LILRA5 NM_021250.3 Endogenous 473 C5orf21 NM_032042.5 Endogenous 474 LOC441073 XR_018937.2 Endogenous 475 TAX1BP1 NM_001079864.2 Endogenous 476 TNFSF13 NM_003808.3 Endogenous 477 PIM2 NM_006875.3 Endogenous 478 RNF19B NM_153341.3 Endogenous 479 EPHX2 NM_001979.5 Endogenous 480 LILRA5 b NM_181879.2 Endogenous 481 ABCF1 NM_001025091.1 Endogenous 482 C4orf27 NM_017867.2 Endogenous 483 PSMB7 NM_002799.2 Endogenous 484 LPCAT4 NM_153613.2 Endogenous 485 TRIM21 NM_003141.3 Endogenous 486 LOC728835 XM_001133190.1 Endogenous 487 NFKB1 NM_003998.3 Endogenous 488 CR2 b NM_001006658.1 Endogenous 489 HMGB2 NM_002129.3 Endogenous 490 IL1B NM_000576.2 Endogenous 491 C20orf52 NM_080748.2 Endogenous 492 DNAJB6 NM_058246.3 Endogenous 493 PFDN5 NM_145897.2 Endogenous 494 RPS6 NM_001010.2 Endogenous 495 LEF1 NM_016269.4 Endogenous 496 DKFZp761P0423 XM_291277.4 Endogenous 497 LOC647340 XR_018104.1 Endogenous 498 FTHL16 XR_041433.1 Endogenous 499 COX6C NM_004374.2 Endogenous 500 BCL10 NM_003921.2 Endogenous 501 CD48 NM_001778.2 Endogenous 502 ZMIZ1 NM_020338.3 Endogenous 503 GZMH NM_033423.4 Endogenous 504 TRRAP NM_003496.3 Endogenous 505 SH2D3C NM_170600.2 Endogenous 506 UBC NM_021009.3 Endogenous 507 TXNDC17 NM_032731.3 Endogenous 508 ATP5J2 NM_004889.3 Endogenous 509 KIAA1267 NM_015443.3 Endogenous 510 RFX1 NM_002918.4 Endogenous 511 WDR1 NM_005112.4 Endogenous 512 LOC100129697 XM_001732822.2 Endogenous 513 TOMM7 NM_019059.2 Endogenous 514 ARHGAP26 NM_015071.4 Endogenous 515 HSPA6 NM_002155.4 Endogenous 516 FLJ10357 NM_018071.4 Endogenous 517 ITGAL NM_002209.2 Endogenous 518 BX089765 BX089765.1 Endogenous 519 RERE NM_001042682.1 Endogenous 520 C15orf39 NM_015492.4 Endogenous 521 BX436458 BX436458.2 Endogenous 522 RWDD1 NM_001007464.2 Endogenous 523 TMBIM6 NM_003217.2 Endogenous 524 SLC6A6 NM_003043.5 Endogenous 525 KIAA0174 NM_014761.3 Endogenous 526 IL16 NM_004513.4 Endogenous 527 EGLN1 NM_022051.1 Endogenous 528 LOC391126 XR_017684.2 Endogenous 529 TAPBP NM_003190.4 Endogenous 530 NUMB NM_001005744.1 Endogenous 531 CENTD2 NM_001040118.2 Endogenous 532 CLSTN1 NM_001009566.2 Endogenous 533 PSMA4 b NM_002789.4 Endogenous 534 LOC648000 XM_371757.4 Endogenous 535 COX7C NM_001867.2 Endogenous 536 PIK3CD NM_005026.3 Endogenous 537 UQCRQ NM_014402.4 Endogenous 538 IDS NM_006123.4 Endogenous 539 C19orf59 NM_174918.2 Endogenous 540 MYL12A NM_006471.3 Housekeeping 541 EIF2B4 NM_015636.3 Housekeeping 542 DGUOK b NM_080916.2 Housekeeping 543 PSMC1 NM_002802.2 Housekeeping 544 CHFR NM_018223.2 Housekeeping 545 ARPC2 NM_005731.2 Housekeeping 546 ATP5B NM_001686.3 Housekeeping 547 RPL3 NM_001033853.1 Housekeeping 548 ZNF143 NM_003442.5 Housekeeping 549 PSMD7 NM_002811.4 Housekeeping 550 TBP NM_003194.4 Housekeeping 551 DHX16 NM_003587.4 Housekeeping 552 TUG1 NR_002323.2 Housekeeping 553 GUSB NM_000181.3 Housekeeping 554 HDAC3 NM_003883.3 Housekeeping 555 SDHA NM_004168.3 Housekeeping 556 PGK1 NM_000291.3 Housekeeping 557 STAMBP NM_006463.4 Housekeeping 558 MTCH1 NM_014341.2 Housekeeping 559 TUBB NM_178014.2 Housekeeping

TABLE II Rank Sequence ID# Gene Class Name 1 TPR NM_003292.2 Endogenous 2 DNAJB1 NM_006145.2 Endogenous 3 PDCD10 NM_145859.1 Endogenous 4 PSMB7 NM_002799.2 Endogenous 5 MERTK NM_006343.2 Endogenous 6 AFTPH NM_017657.4 Endogenous 7 BCOR NM_017745.5 Endogenous 8 RASSF5 NM_182664.2 Endogenous 9 SNX11 NM_152244.1 Endogenous 10 ANP32B NM_006401.2 Endogenous 11 C4B NM_001002029.3 Endogenous 12 NME1-NME2 NM_001018136.2 Endogenous 13 DGUOK NM_080916.2 Endogenous 14 CYP1B1 NM_000104.3 Endogenous 15 MPDU1 NM_004870.3 Endogenous 16 MED16 NM_005481.2 Endogenous 17 FAM179A NM_199280.2 Endogenous 18 CPPED1 NM_018340.2 Endogenous 19 LOC648927 XR_038906.2 Endogenous 20 ANKHD1 NM_017747.2 Endogenous 21 CN312986 CN312986.1 Endogenous 22 PHCA NM_018367.6 Endogenous 23 CD1A NM_001763.2 Endogenous 24 NCOA5 NM_020967.2 Endogenous 25 SLC6A12 NM_003044.4 Endogenous 26 LOC728533 XR_015610.3 Endogenous 27 TRAF3IP2 NM_147686.3 Endogenous 28 TBCE NM_001079515.2 Endogenous 29 CCT6A NM_001762.3 Endogenous 30 P2RY5 NM_005767.5 Endogenous 31 RNASE2 NM_002934.2 Endogenous 32 CLN8 NM_018941.3 Endogenous 33 REPS1 NM_001128617.2 Endogenous 34 TPT1 NM_003295.3 Endogenous 35 LOC100129022 XM_001716591.1 Endogenous 36 KLRC1 NM_002259.3 Endogenous 37 AZI2 NM_022461.4 Endogenous 38 FAM193A NM_003704.3 Endogenous 39 PLAC8 NM_001130715.1 Endogenous 40 LDHA NM_001165416.1 Endogenous 41 GPATCH3 NM_022078.2 Endogenous 42 RBM14 NM_006328.3 Endogenous 43 KYNU NM_001032998.1 Endogenous 44 PPP2R5C NM_178588.1 Endogenous 45 S100A12 b NM_005621.1 Endogenous 46 SFMBT1 NM_001005158.2 Endogenous 47 CCR6 NM_031409.2 Endogenous 48 TRIM39 NM_021253.3 Endogenous 49 AK126342 AK126342.1 Endogenous 50 SLC45A3 NM_033102.2 Endogenous 51 IL4 NM_000589.2 Endogenous 52 UBE2I NM_194259.2 Endogenous 53 PRPF3 NM_004698.2 Endogenous 54 NDUFB3 NM_002491.2 Endogenous 55 CRKL NM_005207.3 Endogenous 56 IDO1 NM_002164.3 Endogenous 57 PUM1 NM_001020658.1 Endogenous 58 BCL10 NM_003921.2 Endogenous 59 TMBIM6 NM_003217.2 Endogenous 60 C17orf51 XM_944416.1 Endogenous 61 BANP NM_079837.2 Endogenous 62 HAVCR2 NM_032782.3 Endogenous 63 BAG3 NM_004281.3 Endogenous 64 DBI NM_001079862.1 Endogenous 65 C4orf27 NM_017867.2 Endogenous 66 TSC1 NM_000368.4 Endogenous 67 LPCAT4 NM_153613.2 Endogenous 68 SAMSN1 NM_022136.3 Endogenous 69 SNORA56 NR_002984.1 Endogenous 70 ARG1 NM_000045.3 Endogenous 71 IL1R2 NM_173343.1 Endogenous 72 CCND3 NM_001760.2 Endogenous 73 USP9Y NM_004654.3 Endogenous 74 ATP2C1 NM_014382.3 Endogenous 75 PSMB1 NM_002793.3 Endogenous 76 NDUFAF2 NM_174889.4 Endogenous 77 VPS37C NM_017966.4 Endogenous 78 HAT1 NM_003642.3 Endogenous 79 LOC732371 XM_001133019.1 Endogenous 80 LOC148137 NM_144692.1 Endogenous 81 CCR1 NM_001295.2 Endogenous 82 CCDC97 NM_052848.1 Endogenous 83 PPP6C NM_002721.4 Endogenous 84 GPI NM_000175.2 Endogenous 85 PIM2 NM_006875.3 Endogenous 86 STAT6 NM_003153.4 Endogenous 87 BATF NM_006399.3 Endogenous 88 EIF4ENIF1 NM_019843.2 Endogenous 89 HSP90AB1 NM_007355.3 Endogenous 90 U2AF2 NM_007279.2 Endogenous 91 CYBB NM_000397.3 Endogenous 92 WDR1 NM_005112.4 Endogenous 93 PSMB8 NM_004159.4 Endogenous 94 TBC1D12 NM_015188.1 Endogenous 95 LOC648000 XM_371757.4 Endogenous 96 XCL2 NM_003175.3 Endogenous 97 PTGDR NM_000953.2 Endogenous 98 ACSL5 NM_203379.1 Endogenous 99 CASP1 NM_033294.3 Endogenous 100 UBTF NM_001076683.1 Endogenous

In one embodiment, a novel gene expression profile or signature can identify and distinguish patients having cancerous tumors from patients having benign nodules. See for example the genes identified in Table I and Table II which may form a suitable gene expression profile. In another embodiment, a portion of the genes of Table I form a suitable profile. In yet another embodiment, a portion of the genes of Table II form a suitable profile. As discussed herein, these profiles are used to distinguish between cancerous and non-cancerous tumors by generating a discriminant score based on differences in gene expression profiles as exemplified below. The validity of these signatures was established on samples collected at different locations by different groups in a cohort of patients with undiagnosed lung nodules. See Example 7 and FIGS. 2A-2B and FIG. 6 . The lung cancer signatures or gene expression profiles identified herein (i.e., Table I or Table II) may be further optimized to reduce the numbers of gene expression products necessary and increase accuracy of diagnosis.

In one embodiment, the composition includes 10 to 559 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In another embodiment, the composition includes 10 to 100 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table II. In another embodiment, the composition includes 10 to 559 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In another embodiment, the composition includes 10 to 100 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table II. In another embodiment, the composition includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, or 559 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In another embodiment, the composition includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table II. In one embodiment, the composition includes at least 3 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 5 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 10 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 15 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 20 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 25 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 30 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 35 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 40 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 45 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 50 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 55 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 60 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 65 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 70 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 75 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 80 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 85 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 90 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 95 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 100 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I or Table II. In one embodiment, the composition includes at least 150 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the composition includes at least 200 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the composition includes at least 250 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the composition includes at least 300 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the composition includes at least 350 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the composition includes at least 400 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the composition includes at least 450 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the composition includes at least 500 polynucleotides or oligonucleotides or ligands, wherein each polynucleotide or oligonucleotide or ligand hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I. In one embodiment, the composition includes polynucleotides or oligonucleotides or ligands capable of hybridizing to each different gene, gene fragment, gene transcript or expression product listed in Table I. In another embodiment, the composition includes polynucleotides or oligonucleotides or ligands capable of hybridizing to each different gene, gene fragment, gene transcript or expression product listed in Table II.

In yet another embodiment, the expression profile is formed by the first 3 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 5 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 10 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 15 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 20 genes in rank order of Table I or Table II. In another embodiment, the expression profile is formed by the first 25 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 30 genes in rank order of Table I or Table II. In another embodiment, the expression profile is formed by the first 35 genes in rank order of Table I or Table II. In another embodiment, the expression profile is formed by the first 40 genes in rank order of Table I or Table II. In another embodiment, the expression profile is formed by the first 45 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 50 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 55 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 60 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 65 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 70 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 75 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 80 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 85 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 90 genes in rank order of Table I or Table II. In yet another embodiment, the expression profile is formed by the first 95 genes in rank order of Table I or Table II. In another embodiment, the expression profile is formed by the first 100 genes in rank order of Table I or Table II. In another embodiment, the expression profile is formed by the first 150 genes in rank order of Table I. In another embodiment, the expression profile is formed by the first 200 genes in rank order of Table I. In another embodiment, the expression profile is formed by the first 250 genes in rank order of Table I. In another embodiment, the expression profile is formed by the first 300 genes in rank order of Table I. In another embodiment, the expression profile is formed by the first 350 genes in rank order of Table I. In another embodiment, the expression profile is formed by the first 400 genes in rank order of Table I. In yet another embodiment, the expression profile is formed by the first 539 genes in rank order of Table I.

As discussed below, the compositions described herein can be used with the gene expression profiling methods which are known in the art. Thus, the compositions can be adapted accordingly to suit the method for which they are intended to be used. In one embodiment, at least one polynucleotide or oligonucleotide or ligand is attached to a detectable label. In certain embodiments, each polynucleotide or oligonucleotide is attached to a different detectable label, each capable of being detected independently. Such reagents are useful in assays such as the nCounter, as described below, and with the diagnostic methods described herein.

In another embodiment, the composition comprises a capture oligonucleotide or ligand, which hybridizes to at least one polynucleotide or oligonucleotide or ligand. In one embodiment, such capture oligonucleotide or ligand may include a nucleic acid sequence which is specific for a portion of the oligonucleotide or polynucleotide or ligand which is specific for the gene of interest. The capture ligand may be a peptide or polypeptide which is specific for the ligand to the gene of interest. In one embodiment, the capture ligand is an antibody, as in a sandwich ELISA.

The capture oligonucleotide also includes a moiety which allows for binding with a substrate. Such substrate includes, without limitation, a plate, bead, slide, well, chip or chamber. In one embodiment, the composition includes a capture oligonucleotide for each different polynucleotide or oligonucleotide which is specific to a gene of interest. Each capture oligonucleotide may contain the same moiety which allows for binding with the same substrate. In one embodiment, the binding moiety is biotin.

Thus, a composition for such diagnosis or evaluation in a mammalian subject as described herein can be a kit or a reagent. For example, one embodiment of a composition includes a substrate upon which the ligands used to detect and quantitate mRNA are immobilized. The reagent, in one embodiment, is an amplification nucleic acid primer (such as an RNA primer) or primer pair that amplifies and detects a nucleic acid sequence of the mRNA. In another embodiment, the reagent is a polynucleotide probe that hybridizes to the target sequence. In another embodiment, the target sequences are illustrated in Table III. In another embodiment, the reagent is an antibody or fragment of an antibody. The reagent can include multiple said primers, probes or antibodies, each specific for at least one gene, gene fragment or expression product of Table I or Table II. Optionally, the reagent can be associated with a conventional detectable label.

In another embodiment, the composition is a kit containing the relevant multiple polynucleotides or oligonucleotide probes or ligands, optional detectable labels for same, immobilization substrates, optional substrates for enzymatic labels, as well as other laboratory items. In still another embodiment, at least one polynucleotide or oligonucleotide or ligand is associated with a detectable label. In certain embodiments, the reagent is immobilized on a substrate. Exemplary substrates include a microarray, chip, microfluidics card, or chamber.

In one embodiment, the composition is a kit designed for use with the nCounter Nanostring system, as further discussed below.

II. GENE EXPRESSION PROFILING METHODS

Methods of gene expression profiling that were used in generating the profiles useful in the compositions and methods described herein or in performing the diagnostic steps using the compositions described herein are known and well summarized in U.S. Pat. No. 7,081,340. Such methods of gene expression profiling include methods based on hybridization analysis of polynucleotides, methods based on sequencing of polynucleotides, and proteomics-based methods. The most commonly used methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization; RNAse protection assays; nCounter® Analysis; and PCR-based methods, such as RT-PCR. Alternatively, antibodies may be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS).

In certain embodiments, the compositions described herein are adapted for use in the methods of gene expression profiling and/or diagnosis described herein, and those known in the art.

A. Patient Sample

The “sample” or “biological sample” as used herein means any biological fluid or tissue that contains immune cells and/or cancer cells. In one embodiment, a suitable sample is whole blood. In another embodiment, the sample may be venous blood. In another embodiment, the sample may be arterial blood. In another embodiment, a suitable sample for use in the methods described herein includes peripheral blood, more specifically peripheral blood mononuclear cells. Other useful biological samples include, without limitation, plasma or serum. In still other embodiment, the sample is saliva, urine, synovial fluid, bone marrow, cerebrospinal fluid, vaginal mucus, cervical mucus, nasal secretions, sputum, semen, amniotic fluid, bronchoalveolar lavage fluid, and other cellular exudates from a subject suspected of having a lung disease. Such samples may further be diluted with saline, buffer or a physiologically acceptable diluent. Alternatively, such samples are concentrated by conventional means. It should be understood that the use or reference throughout this specification to any one biological sample is exemplary only. For example, where in the specification the sample is referred to as whole blood, it is understood that other samples, e.g., serum, plasma, etc., may also be employed in another embodiment.

In one embodiment, the biological sample is whole blood, and the method employs the PaxGene Blood RNA Workflow system (Qiagen). That system involves blood collection (e.g., single blood draws) and RNA stabilization, followed by transport and storage, followed by purification of Total RNA and Molecular RNA testing. This system provides immediate RNA stabilization and consistent blood draw volumes. The blood can be drawn at a physician's office or clinic, and the specimen transported and stored in the same tube. Short term RNA stability is 3 days at between 18-25° C. or 5 days at between 2-8° C. Long term RNA stability is 4 years at −20 to −70° C. This sample collection system enables the user to reliably obtain data on gene expression in whole blood. In one embodiment, the biological sample is whole blood. While the PAXgene system has more noise than the use of PBMC as a biological sample source, the benefits of PAXgene sample collection outweighs the problems. Noise can be subtracted bioinformatically by the person of skill in the art.

In one embodiment, the biological samples may be collected using the proprietary PaxGene Blood RNA System (PreAnalytiX, a Qiagen, BD company). The PAXgene Blood RNA System comprises two integrated components: PAXgene Blood RNA Tube and the PAXgene Blood RNA Kit. Blood samples are drawn directly into PAXgene Blood RNA Tubes via standard phlebotomy technique. These tubes contain a proprietary reagent that immediately stabilizes intracellular RNA, minimizing the ex-vivo degradation or up-regulation of RNA transcripts. The ability to eliminate freezing, batch samples, and to minimize the urgency to process samples following collection, greatly enhances lab efficiency and reduces costs. Thereafter, the miRNA is detected and/or measured using a variety of assays.

B. Nanostring Analysis

A sensitive and flexible quantitative method that is suitable for use with the compositions and methods described herein is the nCounter® Analysis system (NanoString Technologies, Inc., Seattle WA). The nCounter Analysis System utilizes a digital color-coded barcode technology that is based on direct multiplexed measurement of gene expression and offers high levels of precision and sensitivity (<1 copy per cell). The technology uses molecular “barcodes” and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. Each color-coded barcode is attached to a single target-specific probe (i.e., polynucleotide, oligonucleotide or ligand) corresponding to a gene of interest, i.e., a gene of Table I. Mixed together with controls, they form a multiplexed CodeSet. In one embodiment, the CodeSet includes all 559 genes of Table I. In another embodiment, the CodeSet includes all 100 genes of Table II. In another embodiment, the CodeSet includes at least 3 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 5 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 10 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 15 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 20 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 25 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 30 genes of Table I or Table II. In yet another embodiment, the CodeSet includes at least 40 genes of Table I or Table II. In yet another embodiment, the CodeSet includes at least 50 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 60 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 70 genes of Table I or Table II. In yet another embodiment, the CodeSet includes at least 80 genes of Table I or Table II. In yet another embodiment, the CodeSet includes at least 90 genes of Table I or Table II. In another embodiment, the CodeSet includes at least 100 genes of Table I. In another embodiment, the CodeSet includes at least 200 genes of Table I. In another embodiment, the CodeSet includes at least 300 genes of Table I. In yet another embodiment, the CodeSet includes at least 400 genes of Table I. In yet another embodiment, the CodeSet includes at least 500 genes of Table I. In yet another embodiment, the CodeSet is formed by the first 539 genes in rank order of Table I. In yet another embodiment, the CodeSet includes any subset of genes of Table I, as described herein. In another embodiment, the CodeSet includes any subset of genes of Table II, as described herein.

The NanoString platform employs two ˜50 base probes per mRNA that hybridizes in solution. The Reporter Probe carries the signal; the Capture Probe allows the complex to be immobilized for data collection. The probes are mixed with the patient sample. After hybridization, the excess probes are removed and the probe/target complexes aligned and immobilized to a substrate, e.g., in the nCounter Cartridge.

The target sequences utilized in the Examples below for each of the genes of Table I and Table II are shown in Table III below, and are reproduced in the sequence listing. These sequences are portions of the published sequences of these genes. Suitable alternatives may be readily designed by one of skill in the art.

Sample Cartridges are placed in the Digital Analyzer for data collection. Color codes on the surface of the cartridge are counted and tabulated for each target molecule.

A benefit of the use of the NanoString nCounter system is that no amplification of mRNA is necessary in order to perform the detection and quantification. However, in alternate embodiments, other suitable quantitative methods are used. See, e.g., Geiss et al, Direct multiplexed measurement of gene expression with color-coded probe pairs, Nat Biotechnol. 2008 March; 26(3):317-25. doi: 10.1038/nbt1385. Epub 2008 Feb. 17, which is incorporated herein by reference in its entirety.

TABLE III Se- quence  Posi- ID# Gene tion Target Sequence   1 ABCA5 NM_018672.4  6839- AAGGAAGACTGTGTGTAGAATCT  6938 TACGTAATAGTCTGATTCTTTGA CTCTGTGGCTAGAATGACAGTTA TCTATGGAGGTGGTAGAATTAAG CCATACCT   2 ABCF1 NM_00102509  2875- CCTAAACAAACAAGAGGTGACC 1.1  2974 ACCTTATTGTGAGGTTCCATCCA GCCAAGTTTATGTGGCCTATTGT CTCAGGACTCTCATCACTCAGAA GCCTGCCTC   3 ACAA2 NM_006111.2  1605- CTCACTGTGACCCATCCTTACTC  1704 TACTTGGCCAGGCCACAGTAAAA CAAGTGACCTTCAGAGCAGCTGC CACAACTGGCCATGCCCTGCCAT TGAAACAG   4 PHCA NM_018367.6  3324- AGCCAATAGTGATTTGTTTGCAT  3423 ATCACCTAATGTGAAAAGTGCTC ATCTGTGAACTCTACAGCAAATT ATATTTTAGAAAATACTTTGTGA GGCCGGGC   5 ACSL5 NM_203379.1  2701- CTATCACTCATGTCAATCATATC  2800 TATGAGACAAATGTCTCCGATGC TCTTCTGCGTAAATTAAATTGTG TACTGAAGGGAAAAGTTTGATCA TACCAAAC   6 CABC1 NM_020247.4  2536- TTCTAGAGTGAGATTTGTGTTTT  2635 CTGCCCTTTTCCTCTCCAGCCGA TGGGCTGGAGCTGGGAGAGGTGC TGAGCTAACAGTGCCAACAAGT GCTCCTTAA   7 CD97 NM_078481.3  3186- GCCAGTACTCGGGACAGACTAA  3285 GGGCGCTTGTCCCATCCTGGACT TTTCCTCTCATGTCTTTGCTGCA GAACTGAAGAGACTAGGCGCTGG GGCTCAGCT   8 AFTPH NM_017657.4  2741- CTACCACCCGTCCAGTTTGACTG  2840 GAGTAGCAGTGGCCTTACTAACC CTTTAGATGGTGTGGATCCGGAG TTGTATGAGTTAACAACTTCTAA GCTGGAAA   9 AHCYL1 NM_00124267  2401- CTACCCGGCAGGTAGGTTAGATG 6.1  2500 TGGGTGGTGCATGTTAATTTCCC TTAGAAGTTCCAAGCCCTGTTTC CTGCGTAAAGGTGGTATGTCCAG TTCAGAGA  10 AK AK026725.1  1869- AATGAAATTACTGTAGAGTCAGC 026725  1968 AAAGAAGTAGAGAAGAAAAAAC ACCAAGAATGAGGAGAACCTAG CAAGGGCAGGCTTTTGGAAGCA AGAGGTAGATA  11 AK AK093878.1  1554- AGAATTTCTTGGTAGCTTTACAC 093878  1653 CGAAAAATGCGTGTAACTAAAT ACCAGACATCTTGACCATTCAGC TAGAACCCTGGCAGCAACAGAG CTATTTAATT  12 AK AK094576.1  1765- CCCCTCCAGCCAGCCCTGCGTGG 094576  1864 TTGTGGCCCCACTGCAGAAACGC CTCCGCTTAACACTCCAGCCTCT CTTCTATTCGGTCAGGCCACAGC TGCTGACT  13 AK AK124143.1  2252- GTACCTGGTAGAAATTGTGTCTT 124143  2351 GGAATGACCCTTTCGAGTTATTG ACATGGCTCTGATGAATAGAACA TGAGCCCCAAAACTAAATCCAA AAGGAATTT  14 AK AK126342.1  2906- CTTATTGATTAGTGAATGTAGCT 126342  3005 TAAGCCTTTGTATGTGTCCTCAG GGGGCAGACCGACTTTAAGAGG GACCAGATAACGTTTGAATGGA GGGATTATAT  15 AKAP4 NM_139289.1   417- CTGTAAGTGTCCTCAACTGGCTT   516 CTCAGTGATCTCCAGAAGTATGC CTTGGGTTTCCAACATGCACTGA GCCCCTCAACCTCTACCTGTAAA CATAAAGT  16 AKR1C3 NM_003739.4  1097- GAGGACGTCTCTATGCCGGTGAC  1196 TGGACATATCACCTCTACTTAAA TCCGTCCTGTTTAGCGACTTCAG TCAACTACAGCTGAGTCCATAGG CCAGAAAG  17 ALAS1 NM_000688.4  1616- GGGGATCGGGATGGAGTCATGC  1715 CAAAAATGGACATCATTTCTGGA ACACTTGGCAAAGCCTTTGGTTG TGTTGGAGGGTACATCGCCAGCA CGAGTTCTC  18 AMD1 NM_001634.4   572- ACCACCCTCTTGCTGAAAGCACT   671 GGTTCCCCTGTTGAAGCTTGCTA GGGATTACAGTGGGTTTGACTCA ATTCAAAGCTTCTTTTATTCTCG TAAGAATT  19 AMPD3 NM_000480.2  3389- GTGATGCTCAGGGGCTGTCAAAG  3488 TGACTGCGTTCATCAGTTTTACA CTGGGGCTGCTACATAATATTTT CATTTGAACGAAGAACTTCAAAA AGCACAGG  20 ANKHD1 NM_017747.2  7665- CTTGGAACCCTATGATAAAAGTT  7764 ATCCAAAATTCAACTGAATGCAC TGATGCCCAGCAGATTTGGCCTG GCACGTGGGCACCTCATATTGGA AACATGCA  21 ANP32B NM_006401.2   661- CACCTTGGAACCTTTGAAAAAGT   760 TAGAATGTCTGAAAAGCCTGGAC CTCTTTAACTGTGAGGTTACCAA CCTGAATGACTACCGAGAGAGT GTCTTCAAG  22 ANXA1b NM_000700.1   516- GAAATCAGAGACATTAACAGGG   615 TCTACAGAGAGGAACTGAAGAG AGATCTGGCCAAAGACATAACCT CAGACACATCTGGAGATTTTCGG AACGCTTTGC  23 ANXA1 NM_000700.2  1191- TGGATGAAACCAAAGGAGATTA  1290 TGAGAAAATCCTGGTGGCTCTTT GTGGAGGAAACTAAACATTCCCT TGATGGTCTCAAGCTATGATCAG AAGACTTTA  24 AP2S1 NM_021575.3   746- CGAGTAACCGTGCCGTTGTCGTG   845 TGATGCCATAAGCGTCTGTGCGT GGAGTCCCCAATAAACCTGTGGT CCTGCCTGGCCTTGCCGTCAAAA AAAAAAAA  25 CENTD2 NM_00104011  4923- AAACTCCAGAACAGCAGAAAGC 8.2  5022 GGGTGCTGTAGAGGAGCACTCA GCTCACGGGGAGGGAGCTCTTG GCTGAGCTTCTACAGGGCTGAGA GCTGCGCTTTG  26 ARCN1 NM_001655.4  3437- CACTTTTAGCTGGTTGAAAAGTA  3536 CCACTCCCACTCTGAACATCTGG CCGTCCCTGCAAAGAGTGTACTG TGCTTGAAGCAGAGCACTCACAC ATAAATGG  27 ARG1b NM_000045.2   506- AAGGAACTAAAAGGAAAGATTC   605 CCGATGTGCCAGGATTCTCCTGG GTGACTCCCTGTATATCTGCCAA GGATATTGTGTATATTGGCTTGA GAGACGTGG  28 ARG1 NM_000045.3   989- TTCGGACTTGCTCGGGAGGGTAA  1088 TCACAAGCCTATTGACTACCTTA ACCCACCTAAGTAAATGTGGAA ACATCCGATATAAATCTCATAGT TAATGGCAT  29 ARHGAP NM_018054.5  3027- CATGTATGGTCTGTGTCTCCCCA 17  3126 GTCCCCTCAGAACCATGCCCATG GATGGTGACTGCTGGCTCTGTCA CCTCATCAAACTGGATGTGACCC ATGCCGCC  30 ARHGAP NM_033515.2  2499- TTTTTGACCAAAAAGATAACAAA 18  2598 TACCAGGTATGGCAAGTIGTGAA GACAGCACATTAAAACATACCTA ATTTCACAGTATTCCTGTCACGA CAGAATGT  31 ARHGAP NM_015071.4  6088- TCCCTGAGCTTTCCCAGTAGCCT 26  6187 CCAGTTTCCTTTGTAAGACCCAG GGATCACTTAGCCATAGCCTGAA TCTTTTAGGGGTATTAAGGTCAG CCTCTCAC  32 ARHGEF NM_015318.3  5128- GATTACAACATTTCCTCACTGCG 18  5227 GGATATTTCTGACCCGCTTTAGA ACTTAAGACCTGATTCTAGCAAT AAACGTGTCCGAGATGAGCGGT GAAAAAAAA  33 FLJ NM_018071.4  5402- GAATGTGTCTCCTCCACAGTGGC 10357  5501 TCCCAGAGGTTCCACACACTCTC TGAAGCTCCTTCTCCCACACTGC ACCTACTCCTTGAGGCTGAACTG GTCACAGA  34 ARHGEF NM_005435.3  5151- GGGGGACCATTGGGGCCTGAGC 5  5250 CAAGGAACTTTCCTTCTACTGCC TTATAGTGCTTAAACATTCTCCG CCTCCAGGGTGCAGATTCAGAGC TGGCCAGAG  35 ARL8B NM_018184.2  2491- ACCATTACAAAGAATGTGGCAA  2590 CTTGCTTGTGCCTAAAAGGAGGA ATTGGAACTAGAATGTGTGACTC TGTGGGGACTGCATAGGTTTGTT AATTGACCT  36 ARPC2 NM_005731.2   951- ACGGGGAAGACGTTTTCATCCCG  1050 CTAATCTTGGGAATAAGAGGAG GAAGCGGCTGGCAACTGAAGGC TGGAACACTTGCTACTGGATAAT CGTAGCTTTT  37 ASF1B NM_018154.2  1476- CTGTCTCCGGGCCAGGGTCAGGG  1575 ACCCTCTGCCTCTGGCAGCCTTA ACCTGTCCTCTGCTAGGACCAGG GTGATTTCAAGCCAGGGAAGCA ACTGGGACC  38 ATG4B NM_178326.2   106- GGACGCAGCTACTCTGACCTACG   205 ACACTCTCCGGTTTGCTGAGTTT GAAGATTTTCCTGAGACCTCAGA GCCCGTTTGGATACTGGGTAGAA AATACAGC  39 ATG5 NM_004849.2  1105- TGCAGTGGCTGAGTGAACATCTG  1204 AGCTACCCGGATAATTTTCTTCA TATTAGTATCATCCCACAGCCAA CAGATTGAAGGATCAACTATTTG CCTGAACA  40 ATM NM_000051.3    31- ACGCTAAGTCGCTGGCCATTGGT   130 GGACATGGCGCAGGCGCGTTTGC TCCGACGGGCCGAATGTTTTGGG GCAGTGTTTTGAGCGCGGAGACC GCGTGATA  41 ATP2C1 NM_014382.3  4070- TAAAAAGTCCCCAAACCCAAAC  4169 AAATGGTTTATGAACCAGAGTAT ATGTGGAAGATTCTTTGCTGGTC TTGCTCTGTGTGCATCTGAAGCT TCTTTGGCC  42 ATP5B NM_001686.3  1626- CTATATGGTGGGACCCATTGAAG  1725 AAGCTGTGGCAAAAGCTGATAA GCTGGCTGAAGAGCATTCATCGT GAGGGGTCTTTGTCCTCTGTACT GTCTCTCTC  43 ATP5I NM_007100.2   256- TTGCCAGAGAATTGGCAGAAGA   355 TGACAGCATATTAAAGTGAGTGA CCCTGCGACCCACTCTTTGGACC AGCAGCGGATGAATAAAGCTTC CTGTGTTGTG  44 ATP5J2 NM_004889.3   267- GCTGGCATGCTACGTGCTCTTTA   366 GCTACTCCTTTTCCTACAAGCAT CTCAAGCACGAGCGGCTCCGCA AATACCACTGAAGAGGACACAC TCTGCACCCC  45 ATP5L NM_006476.4   196- GGGACGGGGTCCTGCAGCGGGT   295 CCTTCCGGCGGGTGACATTCAGC CGGCGGTTCGGGGCGACGGACT CTCCATTCCAGAACCATGGCCCA ATTTGTCCGT  46 AW AW173314.1   419- AGCAGAAGGCAGGGGAGTCCAC 173314   518 ACAGGGCAAGCAGCAACCAGGC TTCTGAGGACAGGAAAGGAGGG AGCATCTGGTGGGAAGCTGGCG AGGAGGGGCTGG  47 AW AW270402.1   203- GATATCTCACACACGGAATAATC 270402   302 ATTAAGAAACAACCACTGTTGAG CAAAGTTGATAGGCAGTAAGGA AATAAAGTGGACATAAACACAG CAGTACTAAT  48 AZI2 NM_022461.4  3031- GAATTGGTGTCAGATGCTGGAAT  3130 TTATTCTGACCAATGAACACAGC TGACTCAGGGGAGTACAATCTCC TGCCAAGTAATAGAACCAAACC CAATATGCA  49 BACH2 NM_021813.3  8696- TCCAGAACCAGTCTGATGCAAGT  8795 GCACCTCTAATATATGCCTTACA AACTCCAGAGGCCATATTCAAAA CAGGGTCTTCTCAGTGTATGCAA GGGGCTGC  50 BAG3 NM_004281.3  2304- CCCCACCACCTGTTAGCTGTGGT  2403 TGTGCACTGTCTTTTGTAGCTCT GGACTGGAGGGGTAGATGGGGAG TCAATTACCCATCACATAAATAT GAAACATT  51 BANP NM_079837.2  2125- GGAGCCCTTTGCTGTGTGCTCTG  2224 TCCAGTGTCATGAGGCAGGTGTT TGCAAAGCCAGCTCTCGGTTCCG ATGGGGTATTGCTGACCTACTTT TCTAGGGG  52 BATF NM_006399.3   294- CCTGGCAAACAGGACTCATCTGA   393 TGATGTGAGAAGAGTTCAGAGG AGGGAGAAAAATCGTATTGCCG CCCAGAAGAGCCGACAGAGGCA GACACAGAAGG  53 BCL10 NM_003921.2  1251- TGAAAATACCATCTTCTCTTCAA  1350 CTACACTTCCCAGACCTGGGGAC CCAGGGGCTCCTCCTTTGCCACC AGATCTACAGTTAGAAGAAGAA GGAACTTGT  54 BCL6 NM_00113084  3401- CCTCACGGTGCCTTTTTTCACGG 5.1  3500 AAGTTTTCAATGATGGGCGAGCG TGCACCATCCCTTTTTGAAGTGT AGGCAGACACAGGGACTTGAAG TTGTTACTA  55 BCOR NM_017745.5  5794- ATACAAAGCTCTGATGACAGGCC  5893 ATGACTGTAGAGTGGTCAGAACT GTGTGGTTGGTTTGAGGGAGCGA ATTCGGGGAAGGCACTTGGTGAT ATAACTTT  56 BF BF375676.1   141- TGTATTTCTGTGCAATGAGAGAG 375676   240 GCTCTTTATGGTGGTGCTACAAA CAAGCTCATCTTTGGAACTGGCA CTCTGCTTGCTGTCCAGCCAAAT ATCCAGAA  57 BID NM_001196.2  1876- AAGCACGACAGTGGATGCTGGG  1975 TCCATATCACACACATTGCTGTG AACAGGAAACTCCTGTGACCAC AACATGAGGCCACTGGAGACGC ATATGAGTAAG  58 BMPR2 NM_001204.6  1164- CAGCGGCCCTGGCGGGTGCCCTG  1263 GCTACCATGGACCATCCTGCTGG TCAGCACTGCGGCTGCTTCGCAG AATCAAGAACGGCTATGTGCGTT TAAAGATC  59 BQ BQ189294.1   416- GCTGGAGTGATTGGCCCTGATGA 189294   515 CCATGGAGAAAAGAGAGTAGGG AGAACAGTATAACCAGAAGTCA GGGGGGTCTCCTGGAATCCCTCC TCACAATACC  60 BU BU743228.1   154- CCCTGTGGGCCTTGCAGGCCAGT 743228   253 CCAGGCAGGTCTTTCACACTGTT GTCCCACATAACAGAAAAAGCT GAGCAGACAGGGTAGGAAACAC ACTTGCATCT  61 BX BX089765.1   106- TTAAGCAACTTGCTCCAGTGACG 089765   205 CAGCTGGTAAGCAGCAGAGCTG GGATTAAAACCCAGGCATTCTGA TTCCACCACCTACACACTTAGCC ATTCCGCCC  62 BX BX108566.1   365- ATTTAGGGTGAGAGCTTCACAGC 108566   464 TGAAAATCTCCTTTAAAGAAAAC GCGGCCCAAATGTGCTGGGAGG AGAAGCCAGTGGATCTAGGAGG GGGCCCGGCG  63 BX BX400436.2     1- ATATTTTGGAGAGGGAAGTTGGC 400436   100 TCACTGTTGTAGAGGACCTGAAC AAGGTGTTCCCACCCGAGGTCGC TGTGTTTGAGCCATCAGAAGCAG AGATCTCC  64 BX BX436458.2   518- ATGCAGACAATTTGCCTGTGAGA 436458   617 TGAGGAAAATTCTCTGGAAGATT TAGGCCCTGAGAGCTGAAAAGG GACCCTAAACATTACCTGGTGAC AACTGCCCT  65 C15orf NM_015492.4  3535- CCTGAGCTTTTAACGTGAGGGTC 39  3634 TTTATTGGATAGGACTACTCCCT ATTTCTTGCCTAGAGAACACACA TGGGCTTTGGAGCCCGACAGACC TGGGCTTG  66 C17orf XM_944416.1  4909- AAGGATGGGGGTGGATTGACCA 51  5008 AGCTGGGCCAGAGGTGCGAGGA GCTGATCTGCGAGCCCTGTGTGC CTGTGAGTCCTGGCGGAGTGGCC GTGCGTGGTG  67 C3 NM_000064.2  4397- CATCTACCTGGACAAGGTCTCAC  4496 ACTCTGAGGATGACTGTCTAGCT TTCAAAGTTCACCAATACTTTAA TGTAGAGCTTATCCAGCCTGGAG CAGTCAAG  68 C4B NM_00100202  4438- GAGTCCAGGGTGCACTACACCGT 9.3  4537 GTGCATCTGGCGGAACGGCAAG GTGGGGCTGTCTGGCATGGCCAT CGCGGACGTCACCCTCCTGAGTG GATTCCACG  69 C4orf NM_017867.2   682- GAACCGTGAAGATGAAACAGAG 27   781 AGATAAGAAAGTIGTGACAAAG ACCTTTCATGGTGCAGGCTTGGT TGTTCCAGTAGATAAAAATGATG TTGGGTACCG  70 C8orf NM_032847.2  1029- TAAAAGATGAAGTTCACCCAGA 76  1128 GGTGAAGTGTGTTGGCTCCGTAG CCCTGACTGCCTTGGTGACTGTA TCCTCAGAAGAATTTGAAGACAA GTGGTTCAG  71 C9orf NM_182635.1   529- CGCTGGCCATGGGGAAGCCACCT 164   628 CCAGGGCAGTCCCAGGGACTGA ATTGGAAGTTGTCCCAAGTCACT TCAGGTCCAACTGGGACAGCAG AGGTAACCCC  72 CAMP NM_004345.4   623- TTGTCCAGAGAATCAAGGATTTT   722 TTGCGGAATCTTGTACCCAGGAC AGAGTCCTAGTGTGTGCCCTACC CTGGCTCAGGCTTCTGGGCTCTG AGAAATAA  73 CASP1 NM_033294.3   219- ATTTATCCAATAATGGACAAGTC   318 AAGCCGCACACGTCTTGCTCTCA TTATCTGCAATGAAGAATTTGAC AGTATTCCTAGAAGAACTGGAGC TGAGGTTG  74 CASP2 NM_032983.3  3347- CCCACCACTCTTGACTCAGGTGG  3446 TGTCCTTCTTCCTCAAGTCTTGA CAATTCCCGGGCCCTTCAGTCCC TGAGCAGTCTACTTCTGTGTCTG TCACCACA  75 CASP3 NM_032991.2   686- ACTCCACAGCACCTGGTTATTAT   785 TCTTGGCGAAATTCAAAGGATGG CTCCTGGTTCATCCAGTCGCTTT GTGCCATGCTGAAACAGTATGCC GACAAGCT  76 CBLL1 NM_024814.3  1967- ATGAGGGGGAAAAAAACTTATG  2066 TGTAGTCAATCTTTTAAGCTTTG ACTGTTTTGGGAAGGAAGAGTAC CTCTTATCGAGGTAGTATAAAAC ACATAGGGT  77 CC2D1B NM_032449.2  4183- TTGCATAAGCACAGCTCAAGAAC  4282 TGAGCTTTGTATGTGTCCTTTTG GGGGATAACAGGGCTGGACCATG CTTCCCTGCCCTTAAACGCAGAG CTTTTAGT  78 KIAA NM_021174.5   201- GGGAGAGGGCCCACACAGTCTC 1967   300 CTCGCCGGCACCGGCCTCCTCCA TTTTTCCGGGCCTTGCGTGGAGG GTTTTGGCGGATGTTTTTGAACG AAGGAATGT  79 CCDC97 NM_052848.1  2867- ATCCAGAGTGAGACAGCATTGG  2966 AGGGACAAGTGTGCATGCAGAT GTCCTCAGACGGGAAGGTTTGAG AAGGGTCAGATGGTAGGCGGGC CTAACAAGGGC  80 CCL3 NM_002983.2   160- CAGTTCTCTGCATCACTTGCTGC   259 TGACACGCCGACCGCCTGCTGCT TCAGCTACACCTCCCGGCAGATT CCACAGAATTTCATAGCTGACTA CTTTGAGA  81 CCL3L1 NM_021006.4   422- GGAGCCTGAGCCTTGGGAACAT   521 GCGTGTGACCTCTACAGCTACCT CTTCTATGGACTGGTTATTGCCA AACAGCCACACTGTGGGACTCTT CTTAACTTA  82 CCL3L3 NM_00100143   402- GGGGAGGAGCAGGAGCCTGAGC 7.3   501 CTTGGGAACATGCGTGTGACCTC CACAGCTACCTCTTCTATGGACT GGTTATTGCCAAACAGCCACACT GTGGGACTC  83 CCL4 NM_002984.2    36- TTCTGCAGCCTCACCTCTGAGAA   135 AACCTCTTTGCCACCAATACCAT GAAGCTCTGCGTGACTGTCCTGT CTCTCCTCATGCTAGTAGCTGCC TTCTGCTC  84 CCND3 NM_001760.2  1216- GGCCAGCCATGTCTGCATTTCGG  1315 TGGCTAGTCAAGCTCCTCCTCCC TGCATCTGACCAGCAGCGCCTTT CCCAACTCTAGCTGGGGGTGGGC CAGGCTGA  85 CCR1 NM_001295.2   536- CATCATTTGGGCCCTGGCCATCT   635 TGGCTTCCATGCCAGGCTTATAC TTTTCCAAGACCCAATGGGAATT CACTCACCACACCTGCAGCCTTC ACTTTCCT  86 CCR6 NM_031409.2   936- CTTTAACTGCGGGATGCTGCTCC  1035 TGACTTGCATTAGCATGGACCGG TACATCGCCATTGTACAGGCGAC TAAGTCATTCCGGCTCCGATCCA GAACACTA  87 CCR9 NM_031200.1  1096- CCCTGTTCTCTATGTTTTTGTGG  1195 GTGAGAGATTCCGCCGGGATCTC GTGAAAACCCTGAAGAACTTGGG TTGCATCAGCCAGGCCCAGTGGG TTTCATTT  88 CCT6A NM_001762.3   281- GCCCAAGGGCACCATGAAGATG   380 CTCGTTTCTGGCGCTGGAGACAT CAAACTTACTAAAGACGGCAAT GTGCTGCTTCACGAAATGCAAAT TCAACACCCA  89 CD14 NM_000591.2   886- GCCCAAGCACACTCGCCTGCCTT   985 TTCCTGCGAACAGGTTCGCGCCT TCCCGGCCCTTACCAGCCTAGAC CTGTCTGACAATCCTGGACTGGG CGAACGCG  90 CD160b NM_007053.2   501- TTGATGTTCACCATAAGCCAAGT   600 CACACCGTTGCACAGTGGGACCT ACCAGTGTTGTGCCAGAAGCCAG AAGTCAGGTATCCGCCTTCAGGG CCATTTTT  91 CD160 NM_007053.3  1286- AAAGGAAGACAGCCAGATCCAG  1385 TGATTGACTTGGCATGAAAATGA GAAAATGCAGACAGACCTCAAC ATTCAACAACATCCATACAGCAC TGCTGGAGGA  92 CD1A NM_001763.2  1816- CCTGTTTTAGATATCCCTTACTC  1915 CAGAGGGCCTTCCCTGACTTACA AGTGGGAAGCAGTCTCTTCCTGG TCTGAACTCCCGCCACATTTTAG CCGTACTT  93 CD36 NM_000072.3  1619- TAAAGAATCTGAAGAGGAACTA  1718 TATTGTGCCTATTCTTTGGCTTA ATGAGACTGGGACCATTGGTGAT GAGAAGGCAAACATGTTCAGAAG TCAAGTAAC  94 CD48 NM_001778.2   271- AATTTAAAGGCAGGGTCAGACTT   370 GATCCTCAGAGTGGCGCACTGTA CATCTCTAAGGTCCAGAAAGAG GACAACAGCACCTACATCATGA GGGTGTTGAA  95 CD69 NM_001781.2  1360- TATACAGTGTCTTACAGAGAAAA  1459 GACATAAGCAAAGACTATGAGG AATATTTGCAAGACATAGAATAG TGTTGGAAAATGTGCAATATGTG ATGTGGCAA  96 CD70 NM_001252.2   191- CCTATGGGTGCGTCCTGCGGGCT   290 GCTTTGGTCCCATTGGTCGCGGG CTTGGTGATCTGCCTCGTGGTGT GCATCCAGCGCTTCGCACAGGCT CAGCAGCA  97 CD79A NM_021601.3   617- TGAAGATGAAAACCTTTATGAAG   716 GCCTGAACCTGGACGACTGCTCC ATGTATGAGGACATCTCCCGGGG CCTCCAGGGCACCTACCAGGATG TGGGCAGC  98 CD79B NM_000626.2   350- GAAGCTGGAAAAGGGCCGCATG   449 GAAGAGTCCCAGAACGAATCTCT CGCCACCCTCACCATCCAAGGCA TCCGGTTTGAGGACAATGGCATC TACTTCTGT  99 CDC42 NM_006779.3  1779- AGGGCTTTGTGGAGGACAGGCCT EP2  1878 TGCCCTCAAGAACGTCGTACCTG ACGCTGAGCCTGTCATGAGAATG CAACAGGAGCAAACCAAGTGTT GCTGTGACA 100 CDH5 NM_001795.3  3406- TCTCCCCTTCTCTGCCTCACCTG  3505 GTCGCCAATCCATGCTCTCTTTC TTTTCTCTGTCTACTCCTTATCC CTTGGTTTAGAGGAACCCAAGAT GTGGCCTT 101 CDKN1A NM_000389.2  1976- CATGTGTCCTGGTTCCCGTTTCT  2075 CCACCTAGACTGTAAACCTCTCG AGGGCAGGGACCACACCCTGTAC TGTTCTGTGTCTTTCACAGCTCC TCCCACAA 102 CFD NM_001928.2   860- CTGGTTGGTCTTTATTGAGCACC   959 TACTATATGCAGAAGGGGAGGC CGAGGTGGGAGGATCATTGGAT CTCAGGAGTTCGAGATCAGCATG GGCCACGTAG 103 CHCHD3 NM_017812.2  1173- TCCACCCTAACAAAGTAGGATGG  1272 GGTTGGGGGCTAAATTAATTGGA GTGGGGCGAGGAGAGAGCCAGA AAACATAGATCCGAGGGCAGCA GTGCTGGGTG 104 CHFR NM_018223.2  2836- CGCCGCTCCCTCATGCTGCCCGG  2935 GCCCTTCCTCCAAGACCCTACAG AGCCTGAGGGGCACCTTGGCTTC CGCCTGTGCTAGCTTTGCCATGT CATCTGGA 105 CHMP5 NM_016410.5  1148- ACTAAGGAAATGGAATCTTAAA  1247 AGTCTATGACAGTGTAACTCTAC AGTCTCAAAATGACCTGATAAAT TGATAAGACAAAGATGAGATTA TTGGGGCTGT 106 CIAPIN NM_020313.3  1816- GCATGTCTTGTAAAGAGAGGGG 1  1915 ATGTGCATTTGTGTGTGATGTTG GATAGTCATCCACGCTCAGTTTG GACCATTGGAGGAACTTAGTGTC ACGCACAAA 107 CKS2 NM_001827.1   228- AGACTTGGTGTCCAACAGAGTCT   327 AGGCTGGGTTCATTACATGATTC ATGAGCCAGAACCACATATTCTT CTCTTTAGACGACCTCTTCCAAA AGATCAAC 108 CLEC4A NM_194448.2   389- ATTTCTACTGAATCAGCATCTTG   488 GCAAGACAGTGAGAAGGACTGT GCTAGAATGGAGGCTCACCTGCT GGTGATAAACACTCAAGAAGAG CAGGATTTCA 109 CLEC4C NM_203503.1   571- TACGAGAGTATCAACAGTATCAT   670 CCAAGCCTGACCTGCGTCATGGA AGGAAAGGACATAGAAGATTGG AGCTGCTGCCCAACCCCTTGGAC TTCATTTCA 110 CLEC5A NM_013252.2  3251- CCCCATCCAACCCTTAGACTCAC  3350 GAACAAATCCACCTGAGATCAG CAGAGCCACCCTAGATCAGCTGA AACTCTAAGCACAAAAATAAAA ACTTATCACT 111 CLIC3 ILMN_179642    99- CGTACGCCGCTACCTGGACAGCG 3.1   198 CGATGCAGGAGAAAGAGTTCAA ATACACGTGTCCGCACAGCGCCG AGATCCTGGCGGCCTACCGGCCC GCCGTGCAC 112 CLK2 XM_941392.1   552- GATTATAGCCGGGATCGGGGAG   651 ATGCCTACTATGACACAGACTAT CGGCATTCCTATGAATATCAGCG GGAGAACAGCAGTTACCGCAGC CAGCGCAGCA 113 CLN8 NM_018941.3  4486- GGCGCCAGAGCTGGGCTCTTCAA  4585 CACGGCATTTAGCGCAGAAAGTC GTGGTTCAGGCAGTATGGGCCGC TGTGACAAAACACCTAAGACTG GGTAGTTTA 114 CLPTM1 NM_001294.3  2389- TCTGTGTTTCCAGCCATCTCGCC  2488 CTGCCAGCCCAGCACCACTGGGA ATCATGGTGAAGCTGATGCAGCG TTGCCGAGGGGGTGGGTTGGGC GGGGGTGGG 115 CLSTN1 NM_00100956  4990- TTGAATACTGTTCTGTGACCCTG 6.2  5089 ACTGCTAGTTCTGAGGACACTGG TGGCTGTGCTATGTGTGGCCATC CTCCATGTCCCGTCCCTGTAGCT GCTCTGTT 116 CN CN312986.1   491- AGGAAACTAAGACATGGAAAGG 312986   590 TTAGGTAACTTGCCCAAGGTCGC ACAGCTAGTAAGTGGCAGACAT CCAGAGTCTCTGCTCTGCTCTTA ACTCTCACCA 117 CNIH4 NM_014184.3   526- AATGACTGAAGCTGGAGAAGCC   625 GTGGTTGAAGTCAGCCTACACTA CAGTGCACAGTTGAGGAGCCAG AGACTTCTTAAATCATCCTTAGA ACCGTGACCA 118 CNPY2 NM_014255.5  1038- TTGCAGTAAGCGAACAGATCTTT  1137 GTGACCATGCCCTGCACATATCG CATGATGAGCTATGAACCACTGG AGCAGCCCACACTGGCTTGATGG ATCACCCC 119 COLEC NM_130386.2   901- ACACAAGCCAGGCTATCCAGCG 12  1000 AATCAAGAACGACTTTCAAAATC TGCAGCAGGTTTTTCTTCAAGCC AAGAAGGACACGGATTGGCTGA AGGAGAAAGT 120 GLT25 NM_024656.2  3067- CTGTGTGCCAGGCCTCACAGACT D1  3166 CCCAGTTGGGTTGAAGAATGGTT GACTGAGTTTGATTCTTCCTGTA CCCTCGGTCGTCTGAGCTGTGTG CGGACAAC 121 COMMD6 NM_203497.3    32- CTCTCGAGTCCGGGCCGCAAGTC   131 CCAGACGCTGCCCATGGAGGCGT CCAGCGAGCCGCCGCTGGATGCT AAGTCCGATGTCACCAACCAGCT TGTAGATT 122 CORO1C ILMN_174595    98- AAGTAAAGTIGTTGATGGTGGTG 4.1   197 AAACACCGTAGGGCATGTGGTTC AAAGAGAAGCAGGAGGGCAAGG GAAAGTTACCCTGATCTTAGTTT GTAGCTTAT 123 COX6C NM_004374.2    70- GAAGTTTTGCCAAAACCTCGGAT   169 GCGTGGCCTTCTGGCCAGGCGTC TGCGAAATCATATGGCTGTAGCA TTCGTGCTATCCCTGGGGGTTGC AGCTTTGT 124 COX7B NM_001866.2   160- CAGAGCCACCAGAAACGTACAC   259 CTGATTTTCATGACAAATACGGT AATGCTGTATTAGCTAGTGGAGC CACTTTCTGTATTGTTACATGGA CATATGTAG 125 COX7C NM_001867.2     1- CAAGGTCGTGAAAAAAAAGGTC   100 TTGGTGAGGTGCCGCCATTTCAT CTGTCCTCATTCTCTGCGCCTTT CGCAGAGCTTCCAGCAGCGGTAT GTTGGGCCA 126 CPPED1 NM_018340.2  2494- TGTATTTGTTTCTTTACAACAGG  2593 TGTAGGTATAGGAGGTCAAGAAA AGGAGTTCGGTAAAGGGCATAG CTAATAACAACCACACATTGGGC CAGGCACAG 127 CR2b NM_00100665   486- GGTGTCAAGCAAATAATATGTGG 8.1   585 GGGCCGACACGACTACCAACCT GTGTAAGTGTTTTCCCTCTCGAG TGTCCAGCACTTCCTATGATCCA CAATGGACA 128 CR2 NM_00100665  3581- AGCCCAGTTTCACTGCCATATAC 8.2  3680 TCTTCAAGGACTTTCTGAAGCCT CACTTATGAGATGCCTGAAGCCA GGCCATGGCTATAAACAATTACA TGGCTCTA 129 CREB1 NM_004379.3  4856- TTTGATGGTAGGTCAGCAGCAGT  4955 GCTAGTCTCTGAAAGCACAATAC CAGTCAGGCAGCCTATCCCATCA GATGTCATCTGGCTGAAGTTTAT CTCTGTCT 130 CREB5 NM_182898.3  7898- ACCTACTCACCTTTTTCCCTTCT  7997 AAGTTCTGCTAAATCACATCTGC CTCATAGAGAAAGGAATGTTGCC TTTGAGAACTGTCTTGGAGAACA GATAAGCT 131 CRKL NM_005207.3  4901- TTCTAAAGGAGCAGAAGGACAG  5000 GTCTCTGAGACAGGATCGTTGTC CCTACAGGAGGAACAGTGGCCTT GCTTCTTAGACGGTCTTCACTGT GTGTTTTAA 132 CRY2 NM_021117.3  4013- CAGCTCAGGTGGCCCTGAGGGCT  4112 CCCTCGGAACAGTGCCTCAAATC CTGACCCAAGGGCCAGCATGGG GAAGAGATGGTTGCAGGCAAAA TGCACTTTAT 133 CS NM_004077.2  2080- CCTCCTAGCAAGACCTGTTGGTT  2179 AGCTGGACATGCTTTGGCAATTT TTTTATACTACCAAGTGACCATA AAGGCATGGCATTTGTTGTGACT GGCACCCA 134 CSK NM_004383.2  2501- TCTAGGGACCCCTCGCCCCAGCC  2600 TCATTCCCCATTCTGTGTCCCAT GTCCCGTGTCTCCTCGGTCGCCC CGTGTTTGCGCTTGACCATGTTG CACTGTTT 135 CST7 NM_003650.3   618- CAACCACACCTTGAAGCAGACTC   717 TGAGCTGCTACTCTGAAGTCTGG GTCGTGCCCTGGCTCCAGCACTT CGAGGTGCCTGTTCTCCGTTGTC ACTGACCC 136 CTAG1B NM_001327.2   286- GCGGGGCCAGGGGGCCGGAGAG   385 CCGCCTGCTTGAGTTCTACCTCG CCATGCCTTTCGCGACACCCATG GAAGCAGAGCTGGCCCGCAGGA GCCTGGCCCA 137 CTDSP2 NM_005730.3  4685- GAGGTCGGGCCAGCTGCCCCATT  4784 CTTTTAACGTTGTAGGGCCTGCC CATGGAGCGGACCCTCCTCTTTG GGCCTCGTGAGCTTTTTTGCTTA TCATGTTC 138 CTSW NM_001335.3  1076- TGCACCGAGGGAGCAATACCTGT  1175 GGCATCACCAAGTTCCCGCTCAC TGCCCGTGTGCAGAAACCGGATA TGAAGCCCCGAGTCTCCTGCCCT CCCTGAAC 139 CTSZ NM_001336.3  1174- CACTGGCTGCGAGTGTTCCTGAG  1273 AGTTGAAAGTGGGATGACTTATG ACACTTGCACAGCATGGCTCTGC CTCACAATGATGCAGTCAGCCAC CTGGTGAA 140 CX3CL1 NM_002996.3   141- AGCACCACGGTGTGACGAAATG   240 CAACATCACGTGCAGCAAGATG ACATCAAAGATACCTGTAGCTTT GCTCATCCACTATCAACAGAACC AGGCATCATG 141 CXCL2 NM_002089.3   855- ATCACATGTCAGCCACTGTGATA   954 GAGGCTGAGGAATCCAAGAAAA TGGCCAGTGAGATCAATGTGACG GCAGGGAAATGTATGTGTGTCTA TTTTGTAAC 142 IL8RB NM_001557.3   410- ACCTCAAAAATGGAAGATTTTAA   509 CATGGAGAGTGACAGCTTTGAA GATTTCTGGAAAGGTGAAGATCT TAGTAATTACAGTTACAGCTCTA CCCTGCCCC 143 CXCR5b NM_001716.3  2619- ACGTCCCTTTTTTCTCTGAGTAT  2718 CTCCTCGCAAGCTGGGTAATCGA TGGGGGAGTCTGAAGCAGATGCA AAGAGGCAAGAGGCTGGATTTT GAATTTTCT 144 CYBB NM_000397.3  3787- ACTGGAGAGGGTACCTCAGTTAT  3886 AAGGAGTCTGAGAATATTGGCCC TTTCTAACCTATGTGCATAATTA AAACCAGCTTCATTTGTTGCTCC GAGAGTGT 145 CYP1B1 NM_000104.3  2361- CTTACACCAAACTACTGAATGAA  2460 GCAGTATTTTGGTAACCAGGCCA TTTTTGGTGGGAATCCAAGATTG GTCTCCCATATGCAGAAATAGAC AAAAAGTA 146 DB DB338252.1   436- GTTCTTGGTCTGTATGTGTAGGT 338252   535 GGAGGGAGGCAAAGTTGTGGTA ATAAAGTGGGAAGGCCCGGGAA GAACAGCTAACTGTATAGGGGT GAAATGACGCT 147 DBI NM_00107986   241- CATAAATACAGAACGGCCCGGG 2.1   340 ATGTTGGACTTCACGGGCAAGGC CAAGTGGGATGCCTGGAATGAG CTGAAAGGGACTTCCAAGGAAG ATGCCATGAAA 148 DCAF7 NM_005828.4  6155- TTAACACTGTGCTGTGAAACAAC  6254 TATGGGGAATCTCCATTGAAGGC TACTTCATGGGCACCTGAAAGTG GAGTGTTATAGCTATGACTTTCT ATTTCTTG 149 DDIT4 NM_019058.2  1414- GACCTGTTGTAGGCAGCTATCTT  1513 ACAGACGCATGAATGTAAGAGT AGGAAGGGGTGGGTGTCAGGGA TCACTTGGGATCTTTGACACTTG AAAAATTACA 150 DDX23 NM_004818.2  2811- ATTGCACTGGGCCATCAGCTCAT  2910 GCCAGGCTATGGGGGCAGCCAG TTGGCATTGCTCCCCAGACTGAA CAGAAACCTGGCCGCCGGATGG GACCTCCTTT 151 DGUOK NM_080916.2   573- ACATCGAGTGGCATATCTATCAG   672 GACTGGCATTCTTTTCTCCTGTG GGAGTTTGCCAGCCGGATCACAT TACATGGCTTCATCTACCTCCAG GCTTCTCC 152 DGUOKb NM_080916.2   903- TTGTAAAGAATCTGTAACCAATA  1002 CCATGAAGTTCAGGCTGTGATCT GGGCTCCCTGACTTTCTGAAGCT AGAAAAATGTTGTGTCTCCCAAC CACCTTTC 153 DHX16b NM_00116423  2491- CCCGTGTCAACTTCTTTCTCCCT 9.1  2590 GGCGGTGACCACCTGGTTCTGCT AAATGTTTACACACAGTGGGCTG AGAGTGGTTACTCTTCCCAGTGG TGCTATGA 154 DHX16 NM_003587.4  3189- ACCAAAGAGTTCATGAGACAGG  3288 TACTGGAGATTGAGAGCAGTTGG CTTCTGGAGGTGGCTCCCCATTA TTATAAGGCCAAGGAGCTAGAA GATCCCCATG 155 DKFZp XM_291277.4  4192- CTCCTGCAGCTTCTGTGAGCCAA 761P04  4291 GCCCCAGCCTGCACCGTCGCTGC 23 CCCTTCCCTGCCTAACCCTTTCC TGTCTCGCCTTGGAAGCACCCAT GTCTCCCT 156 DMBT1 NM_007329.2  3713- CACAATGGCTGGCTCTCCCACAA  3812 CTGTGGCCATCATGAAGACGCTG GTGTCATCTGCTCAGCTTCCCAG TCCCAGCCGACACCCAGCCCAGA CACTTGGC 157 DNAJB1 NM_006145.2  1904- GACCTCTGGCTCCAGTGAAGCTG  2003 AATGTCCTCACTTTGTGGGTCAC ACTCTTTACATTTCTGTAAGGCA ATCTTGGCACACGTGGGGCTTAC CAGTGGCC 158 DNAJB6 NM_058246.3  2087- CTTCCCTGCATGCTCCCTCCCAG  2186 TGACTTTCCTTCCCTTTCACATG AGGATCTGCCGTTCATGTTGCTT TCTCCTTTGTCCTCTTGGACTTG AGGGCATT 159 DOCK5 NM_024940.6  7201- AAAGAGATTTCCATTTCTGCTGC  7300 CAGAGCTGGTATTTGCCTGCCTG ATTCTCTGTGTTTCCTGTTTCAC CGCCACCCTTTCAGGAGAGAACT ACACCAGT 160 DPF2 NM_006268.4  2249- TCTCAGCTCATGGGGAAGCCACA  2348 TAGACATCCCTTTCTTCCCTTGC ACGCTCGCTAGCAGCTGGTAAGG TCTTCACACCCTGATTCCTCAAG TTTTCTGC 161 DYNC2L NM_016008.3   351- TTTGGGAACTCGGTGGAGGAACC I1   450 TCTTTATTGGACTTAATCAGCAT ACCCATCACAGGTGACACCTTAC GGACGTTTTCTCTTGTTCTCGTT CTGGATCT 162 DZIP3 NM_014648.3  4323- CCCAGTGTCTTGCCCAGTAGATA  4422 CAAGATAAATATTGCCAGAATCA GATATCAGGAAGTAGTAAGAAA AGGAGTTAATATGCAAACTAAAT CACTCGCTC 163 EEF1B2 NM_00103766   699- GGATACGGAATTAAGAAACTTC 3.1   798 AAATACAGTGTGTAGTTGAAGAT GATAAAGTTGGAACAGATATGCT GGAGGAGCAGATCACTGCTTTTG AGGACTATG 164 EGLN1 NM_022051.1  3976- AGCAGCATGGACGACCTGATAC  4075 GCCACTGTAACGGGAAGCTGGG CAGCTACAAAATCAATGGCCGG ACGAAAGCCATGGT 165 EGR1 NM_001964.2  1506- GAGGCATACCAAGATCCACTTGC  1605 GGCAGAAGGACAAGAAAGCAGA CAAAAGTGTTGTGGCCTCTTCGG CCACCTCCTCTCTCTCTTCCTAC CCGTCCCCG 166 EHD4 NM_139265.3  2605- TCAAACATTAAATATCCCGAGGT  2704 CTCCTTGGTGGGTGGCAGGATTT AAATTCAATCAAATCCTGTCCTA GTGTGTGCAGTGTCTTCGGCCCT GTGGACAC 167 EID2B NM_152361.2   628- GCCAGTTTAGTTAACTCAGTCAT   727 TAGGGGGAATGCAAACTGGAAG GGAATACGGCAATGTGCAATTG AAGGAGGAAGCACACTCCGAAA TGGAAACAGAC 168 EIF2B4 NM_015636.3  1497- GTCTCTAATGAGCTAGATGACCC  1596 TGATGATCTGCAATGTAAGCGGG GAGAACATGTTGCGCTGGCTAAC TGGCAGAACCACGCATCCCTACG GTTGTTGA 169 EIF4EN NM_019843.2  3051- CACACTGGGCAGGACCCTGCTTC IF1  3150 ATCTCGGGTTGGTTTATGGGCTT TTACTTTGGAGCACTCTGTGTGA AGCTGTTTGGTGGAACCCATGCA TCTGGTGT 170 EMR4 NM_00108049  1719- GGGAAGACGATTGGATCAATCA 8.2  1818 TTGCATACTCATTCACCATCATC AACACCCTTCAGGGAGTGTTGCT CTTTGTGGTACACTGTCTCCTTA ATCGCCAGG 171 EP300 NM_001429.2   716- CCAGCCAGGCCCAACAGAGCAG   815 TCCTGGATTAGGTTTGATAAATA GCATGGTCAAAAGCCCAATGAC ACAGGCAGGCTTGACTTCTCCCA ACATGGGGAT 172 EPHX2 NM_001979.5  1909- CATCCTTCCACCTGCTGGGGCAC  2008 CATTCTTAGTATACAGAGGTGGC CTTACACACATCTTGCATGGATG GCAGCATTGTTCTGAAGGGGTTT GCAGAAAA 173 ERLIN1 NM_006459.3  3197- TGATGGCCCTGGAGGCGGGGCT  3296 GAGGAACAGGGAAATGCCGCTG TGAAGTCTTAAAGCACTTCTGCT TAAACTCCCATGTGTGAGGAGTG TGCCTCCCTG 174 ETFDH NM_004453.3  1904- TGACCTCTTGTCATCTGTGGCTC  2003 TGAGTGGTACTAATCATGAACAT GACCAGCCGGCACACTTAACCTT AAGGGATGACAGTATACCTGTAA ATAGAAAT 175 EVI2A NM_014210.3  1410- GAGAGAGCTAAACTGTGTAATTT  1509 AATGGTATCTTCCTTGCTGGATG TGGCAGAATCCACACCAGCTTAT CAACCAACACAGCTAATTTTAGA ATAGATCC 176 EWSR1 NM_005243.3  2248- AAAAATGGATAAAGGCGAGCAC  2347 CGTCAGGAGCGCAGAGATCGGC CCTACTAGATGCAGAGACCCCGC AGAGCTGCATTGACTACCAGATT TATTTTTTAA 177 EYA3 NM_001990.3  1551- GATTCCTGGTTAGGAACTGCATT  1650 AAAGTCCTTACTTCTCATCCAGT CCAGAAAGAATTGTGTGAATGTT CTGATCACTACCACCCAGCTGGT TCCAGCCC 178 C5orf NM_032042.5  4058- TTAGAACAAGTAGAATGGGAAA 21  4157 GGAGTGACTGATAAATCTAAGAT TCAAAATAGTCCCGTCGAAACTT AAAGGCCAGATTATTGCTTTGGA GCTTTCTAT 179 FAM NM_199280.2  3306- ACTCTTAGACTCAGAGTCCTTGG 179A  3405 GAGGCAGCCGCAAGGCCACTGA CAGAGGGGTGGCCCCTGACAGC AAGACAACTGGCAGCTCATACCC TTTTCAGCTG 180 FAM NM_003704.3  4523- CCCTGACTTGTAGCCAGCTTGTG 193A  4622 TAAGATCCCTTGCAGAACGAGA AAGTTAAAAACAAGCCCACCCA GTACTCACACCATCAAGTCTGTT ATAGAGTGTA 181 FAM43A NM_153690.4  2741- AGACCCCTGAAATGTTGCCAAAT  2840 TCTTCAAATAACTGTTTGGGGGG TGGGGGGAGATGAAAGAGAGTC GCGTTTTGTTTACAGTTAAAGAC ATCCAATAT 182 FAM50B NM_012135.1  1273- TTCTGAGTATTTTAGTGTTGCCA  1372 CCTGGATTTGCTGCATTGCTCTG CTGAGCTGTATTGAAACCATGAC TGGGCCCACTGTCAGACAGAAAT TAGAATAG 183 FAIM3 NM_005449.4  1689- CAGGCTCTAGATCACATGGCATC  1788 AGGCTGGGGCAGAGGCATAGCT ATTGTCTCGGGCATCCTTCCCAG GGTTGGGTCTTACACAAATAGAA GGCTCTTGC 184 FKBP1A NM_054014.3   301- AGAAACAAGCCCTTTAAGTTTAT   400 GCTAGGCAAGCAGGAGGTGATC CGAGGCTGGGAAGAAGGGGTTG CCCAGATGAGTGTGGGTCAGAG AGCCAAACTGA 185 FLNB NM_001457.3  9148- CAGACCTGAGCTGGCTTTGGAAT  9247 GAGGTTAAAGTGTCAGGGACGTT GCCTGAGCCCAAATGTGTAGTGT GGTCTGGGCAGGCAGACCTTTAG GTTTTGCT 186 FNBP1 NM_015033.2  5237- TGTGTGTTGCACTAATTCTAAAC  5336 TTTGGAGGCATTTTGCTGTGTGA GGCCGATCGCCACTGTAAAGGTC CTAGAGTTGCCTGTTTGTCTCTG GAGATGGA 187 FOXK2 NM_004514.3  4387- TTTTTTGCCGTAGGCACCATTCT  4486 GCATCTTGAACCCAGACTGAAGT GTGCCTCTCACAGATGGAAGGTG CACACGCTCCTGTCTCCTCCTCA CTCTGCCA 188 FRAT2 NM_012083.2  1769- CTTGTCCTCCCAGCTGAGCTTTC  1868 TTATTCCACCCTTTCTGGTGTCT ATAGGAATGCATGAGAGACCCTG GACGTTTTTCTGCTCTCTTCTGG CCCTCCAT 189 FTHL16 XR_041433.1   255- GGACTCAGAGGCCGCCATCAAC   354 CGCCAGATCAACCTAGAGCTCTG TGCCTCCTACGTTTACCTGTCCA TGTCTTACTGCTTTGACCGTGAT GATGTGGCT 190 GATA2 NM_00114566  2573- GTCCAGTTGATTGTACGTAGCCA 2.1  2672 CAGGAGCCCTGCTATGAAAGGA ATAAAACCTACACACAAGGTTG GAGCTTTGCAATTCTTTTTGGAA AAGAGCTGGG 191 GLIS3 NM_00104241   548- ACTCGCGCTGGCCGGCCGGGGG 3.1   647 AAGGGACCCGCACGCCGGGCTTT GTTGTGGAAATCCCGGTTACCTG GCTTATAACCCACACCATGGATA ACTTATTGG 192 GLRX ILMN_173730   119- AAAGCATAGTTGGTCTTGGTGTC 8.1   218 ATATGGATCAGAGGCACAAGTG CAGAGGCTGTGGTCATGCGGAA CACTCTGTTATTTAAGATGGCTA TCCAGATAAT 193 GNL3 NM_014366.4  1733- TACAGCAGGTGAACAGTCTACA  1832 AGGTCTTTTATCTTGGATAAAAT CATTGAAGAGGATGATGCTTATG ACTTCAGTACAGATTATGTGTAA CAGAACAAT 194 GNS NM_002076.3  4988- CCTGTGTTTGCATCCTCTGTTCC  5087 TATTCTGCCCTTGCTCTGTGTCA TCTCAGTCATTTGACTTAGAAAG TGCCCTTCAAAAGGACCCTGTTC ACTGCTGC 195 GOLGA3 NM_005895.3  8961- CTCACTGACCGGAAGGTCCAGGT  9060 GAATCTCGTCATAAGTGATCTCA GGCTCTCACAGGATCCGGAGGG AAATGTGTTAGAGGGTCTGGAA AATTCAGTGC 196 GPAT NM_022078.2  1686- AGTCTGGGAGCAGCAGTCTTCGT CH3  1785 GGCTGGTTCAGGGTGTTTTGTTC CGAGCCTGCCTGCCTGCCGGTTC TATACCTCAGGGGCATTTTTACA AAAAGCCC 197 GPI NM_000175.2  1696- CAGTGCTCAAGTGACCTCTCACG  1795 ACGCTTCTACCAATGGGCTCATC AACTTCATCAAGCAGCAGCGCG AGGCCAGAGTCCAATAAACTCGT GCTCATCTG 198 GPR65 NM_003608.3  1899- TATGATTTTTCTCACTCTTTCTT  1998 TGGACTCCAGGGTGTCAGCCATC AGGTCTCCTAATTTTGTGTACCG GTCTCCAACAACCCCAGCTACTG AATACTGC 199 GSTO1 NM_004832.2   897- AGAGCTCTACTTACAGAACAGCC   996 CTGAGGCCTGTGACTATGGGCTC TGAAGGGGGCAGGAGTCAGCAA TAAAGCTATGTCTGATATTTTCC TTCACTAAT 200 GUSB NM_000181.3  2032- GGTATCCCCACTCAGTAGCCAAG  2131 TCACAATGTTTGGAAAACAGCCT GTTTACTTGAGCAAGACTGATAC CACCTGCGTGTCCCTTCCTCCCC GAGTCAGG 201 GZMA NM_006144.3   636- GCCTCCGAGGTGGAAGAGACTC   735 GTGCAATGGAGATTCTGGAAGCC CTTTGTTGTGCGAGGGTGTTTTC CGAGGGGTCACTTCCTTTGGCCT TGAAAATAA 202 GZMB NM_004131.3   541- ACACTACAAGAGGTGAAGATGA   640 CAGTGCAGGAAGATCGAAAGTG CGAATCTGACTTACGCCATTATT ACGACAGTACCATTGAGTTGTGC GTGGGGGACC 203 GZMH NM_033423.4   718- GGCCCCTCGTGTGTAAGGACGTA   817 GCCCAAGGTATTCTCTCCTATGG AAACAAAAAAGGGACACCTCCA GGAGTCTACATCAAGGTCTCACA CTTCCTGCC 204 HAT1 NM_003642.3  1235- AACCAAATAGAAATAAGCATGC  1334 AACATGAACAGCTGGAAGAGAG TTTTCAGGAACTAGTGGAAGATT ACCGGCGTGTTATTGAACGACTT GCTCAAGAGT 205 HAVCR2 NM_032782.3   956- TATATGAAGTGGAGGAGCCCAA  1055 TGAGTATTATTGCTATGTCAGCA GCAGGCAGCAACCCTCACAACCT TTGGGTTGTCGCTTTGCAATGCC ATAGATCCA 206 HDAC3 NM_003883.3  1765- AAGATGAAGAGAGAGAGATTTG  1864 GAAGGGGCTCTGGCTCCCTAACA CCTGAATCCCAGATGATGGGAA GTATGTTTTCAAGTGTGGGGAGG ATATGAAAAT 207 HERC1 NM_003922.3 14664- CAATCGACATGGACAACTACATG 14763 CTCTCGAGAAACGTGGACAACG CCGAGGGCTCCGACACTGACTAC TGACCGTGCGGGTGCTCTCACCC TCCCTTCTC 208 HERC3 NM_014606.2  3796- TAAGAATGATTTAGACTGACCTG  3895 TCCTTTTTTATCTGCGCATGCGA GAACATCACCTTCCTCTGTACAC TTGGAAATGCCTCTGGCTTGTTG CAGCCCTC 209 HK3 NM_002115.2  2785- AGTCAGAGGATGGGTCCGGCAA  2884 AGGTGCGGCCCTGGTCACCGCTG TTGCCTGCCGCCTTGCGCAGTTG ACTCGTGTCTGAGGAAACCTCCA GGCTGAGGA 210 HLA-B NM_005514.6   938- CCCTGAGATGGGAGCCGTCTTCC  1037 CAGTCCACCGTCCCCATCGTGGG CATTGTTGCTGGCCTGGCTGTCC TAGCAGTTGTGGTCATCGGAGCT GTGGTCGC 211 HLA- NM_002118.3    21- CCCGTGAGCTGGAAGGAACAGA DMB   120 TTTAATATCTAGGGGCTGGGTAT CCCCACATCACTCATTTGGGGGG TCAAGGGACCCGGGCAATATAG TATTCTGCTC 212 HLA-G NM_002127.4  1181- AAGAGCTCAGATTGAAAAGGAG  1280 GGAGCTACTCTCAGGCTGCAATG TGAAACAGCTGCCCTGTGTGGGA CTGAGTGGCAAGTCCCTTTGTGA CTTCAAGAA 213 HMGB1 NM_002128.4   209- TATGCATTTTTTGTGCAAACTTG   308 TCGGGAGGAGCATAAGAAGAAGC ACCCAGATGCTTCAGTCAACTTC TCAGAGTTTTCTAAGAAGTGCTC AGAGAGGT 214 HMGB2 NM_002129.3   670- TGCTGCATATCGTGCCAAGGGCA   769 AAAGTGAAGCAGGAAAGAAGGG CCCTGGCAGGCCAACAGGCTCA AAGAAGAAGAACGAACCAGAAG ATGAGGAGGAG 215 HNRNP NM_004499.3  1246- CCCCATGGAAATCACTCTCCTGT AB  1345 TGACTATTTCCAGAGCTCTAGGT GTTTAGGCAGCGTGTGGTGTCTG AGAGGCCATAGCGCCATCATGG GCTGATTTT 216 HNRNPK NM_031263.2   538- TCCCTACCTTGGAAGAGGGCCTG   637 CAGTTGCCATCACCCACTGCAAC CAGCCAGCTCCCGCTCGAATCTG ATGCTGTGGAATGCTTAAATTAC CAACACTA 217 HOOK3 NM_032410.3  2391- GCAAGGTAGAGAAGTTGTGCCG  2490 CTCAATCACAGACACCTGCACCC ACAACATACTTCTGTTACACACA AGAACATTTCAGGAAACTCAGCC AGCTTATTT 218 HOPX NM_139211.4   590- AACAATAGGAAGCTATGTGTATC   689 TTCTGTGTAAAGCAGTGGCTTCA CTGGAAAAATGGTGTGGCTAGC ATTTCCCTTTGAGTCATGATGAC AGATGGTGT 219 HPSE NM_006665.5  3920- GAGGTTCCTATAATTGTCTCTGA  4019 GTAACCCTTTGGAATGGAGAGG GTGTTGGTCAGTCTACAAACTGA ACACTGCAGTTCTGCGCTTTTTA CCAGTGAAA 220 HSCB NM_172002.3   343- TCCACCCAGATTTCTTCAGCCAG   442 AGGTCTCAGACTGAAAAGGACTT CTCAGAGAAGCATTCGACCCTGG TGAATGATGCCTATAAGACCCTC CTGGCCCC 221 HSD11B NM_181755.1   156- GCCTACTACTACTATTCTGCAAA 1   255 CGAGGAATTCAGACCAGAGATG CTCCAAGGAAAGAAAGTGATTG TCACAGGGGCCAGCAAAGGGAT CGGAAGAGAGA 222 HSP90A NM_007355.3  1531- GGCATTCTCTAAAAATCTCAAGC B1  1630 TTGGAATCCACGAAGACTCCACT AACCGCCGCCGCCTGTCTGAGCT GCTGCGCTATCATACCTCCCAGT CTGGAGAT 223 HSPA6 NM_002155.4  1990- GTGGCACTCAAGCCCGCCAGGG  2089 GGACCCCAGCACCGGCCCCATCA TTGAGGAGGTTGATTGAATGGCC CTTCGTGATAAGTCAGCTGTGAC TGTCAGGGC 224 HUWE1 NM_031407.6 13637- CCACCAACTCACCGTGTGTGTCC 13736 CAGCTGCCCCATCTTCCCCAGCG CATACCTGTTCCTCTTCTCATTC TCTCCCCGCCGCCTGTTTCCTCA CCTTCTCT 225 HVCN1 NM_032369.3   747- TGTTCCAGGAGCACCAGTTTGAG   846 GCTCTGGGCCTGCTGATTCTGCT CCGGCTGTGGCGGGTGGCCCGG ATCATCAATGGGATTATCATCTC AGTTAAGAC 226 IDO1 NM_002164.3    51- CTATTATAAGATGCTCTGAAAAC   150 TCTTCAGACACTGAGGGGCACCA GAGGAGCAGACTACAAGAATGG CACACGCTATGGAAAACTCCTGG ACAATCAGT 227 IDS NM_006123.4  1016- TGGATGGACATCAGGCAACGGG  1115 AAGACGTCCAAGCCTTAAACATC AGTGTGCCGTATGGTCCAATTCC TGTGGACTTTCAGCGGAAAATCC GCCAGAGCT 228 IER5 NM_016545.4  1802- ACTTTACACCTACCCCTCACCGG  1901 AAAGCTAGACCCGCTTCAGGGCC AGGAGTGGCGTTTCCGCACAGG ATTTCCTAAGACGAGAGGGATTT AGCCAAGAG 229 IFI27 NM_032036.2   305- GTCAGTGTTGGGGGCCTGCTTGG L2   404 GGAATTCACCTTCTTCTTCTCTC CCAGCTGAACCCGAGGCTAAAGA AGATGAGGCAAGAGAAAATGTA CCCCAAGGT 230 IFNA17 NM_021268.2   292- TGAGATGATCCAGCAGACCTTCA   391 ATCTCTTCAGCACAGAGGACTCA TCTGCTGCTTGGGAACAGAGCCT CCTAGAAAAATTTTCCACTGAAC TTTACCAG 231 IFNAR1 NM_000629.2  3124- CTAATCAGCTCTCAGTGATCAAC  3223 CCACTCTTGTTATGGGTGGTCTC TGTCACTTTGAATGCCAGGCTGG CTTCTCGTCTAGCAGTATTCAGA TACCCCTT 232 IFNAR2 NM_000874.3   632- AAATACCACAAGATCATTTTGTG   731 ACCTCACAGATGAGTGGAGAAG CACACACGAGGCCTATGTCACCG TCCTAGAAGGATTCAGCGGGAA CACAACGTTG 233 IFNGR1 NM_000416.1  1141- CCCGGGCAGCCATCTGACTCCAA  1240 TAGAGAGAGAGAGTTCTTCACCT TTAAGTAGTAACCAGTCTGAACC TGGCAGCATCGCTTTAAACTCGT ATCACTCC 234 IGFBP7 NM_001553.2   584- ATCGGAATCCCGACACCTGTCCT   683 CATCTGGAACAAGGTAAAAAGG GGTCACTATGGAGTTCAAAGGAC AGAACTCCTGCCTGGTGACCGGG ACAACCTGG 235 IL16 NM_004513.4  1263- GGCATCTCCAACATCATCATCCA  1362 ACGAAGACTCAGCTGCAAATGG TTCTGCTGAAACATCTGCCTTGG ACACAGGGTTCTCGCTCAACCTT TCAGAGCTG 236 IL1B NM_000576.2   841- GGGACCAAAGGCGGCCAGGATA   940 TAACTGACTTCACCATGCAATTT GTGTCTTCCTAAAGAGAGCTGTA CCCAGAGAGTCCTGTGCTGAATG TGGACTCAA 237 ILIR2 NM_173343.1   114- TGCTTCTGCCACGTGCTGCTGGG   213 TCTCAGTCCTCCACTTCCCGTGT CCTCTGGAAGTTGTCAGGAGCAA TGTTGCGCTTGTACGTGTTGGTA ATGGGAGT 238 IL4 NM_000589.2   626- GACACTCGCTGCCTGGGTGCGAC   725 TGCACAGCAGTTCCACAGGCACA AGCAGCTGATCCGATTCCTGAAA CGGCTCGACAGGAACCTCTGGG GCCTGGCGG 239 IL7 NM_000880.2    39- AATAACCCAGCTTGCGTCCTGCA   138 CACTTGTGGCTTCCGTGCACACA TTAACAACTCATGGTTCTAGCTC CCAGTCGCCAAGCGTTGCCAAGG CGTTGAGA 240 INTS4 NM_033547.3   652- CCCACGTGTCAGAACAGCAGCTA   751 TAAAAGCCATGTTGCAGCTCCAT GAAAGAGGACTGAAATTACACC AAACAATTTATAATCAGGCCTGT AAATTACTC 241 IRAK2 NM_001570.3  1286- GTGTTGGCCGAGGTCCTCACGGG  1385 CATCCCTGCAATGGATAACAACC GAAGCCCGGTTTACCTGAAGGAC TTACTCCTCAGTGATATTCCAAG CAGCACCG 242 IRF1 NM_002198.1   511- CTGTGCGAGTGTACCGGATGCTT   610 CCACCTCTCACCAAGAACCAGAG AAAAGAAAGAAAGTCGAAGTCC AGCCGAGATGCTAAGAGCAAGG CCAAGAGGAA 243 IRF4 NM_002460.1   326- GGGCACTGTTTAAAGGAAAGTTC   425 CGAGAAGGCATCGACAAGCCGG ACCCTCCCACCTGGAAGACGCGC CTGCGGTGCGCTTTGAACAAGAG CAATGACTT 244 KIAA NM_014761.3  2187- ATGGATGGGACTCTTATGTCATA 0174  2286 ACTTCTGTTACTCCTTTGGCCCA TAGCTAAGGTCATCCTTCCCCAC AGGGGTGGCTTTGGGATTGGATG ATACAGCT 245 ITCH NM_00125713   439- GAGGTGACAAAGAGCCAACAGA 8.1   538 GACAATAGGAGACTTGTCAATTT GTCTTGATGGGCTACAGTTAGAG TCTGAAGTTGTTACCAATGGTGA AACTACATG 246 ITFG2 NM_018463.3  1985- GTCTGGTCTTACCCATGTTCCTA  2084 GCAACCCTGAGATGATTTTCTTC CATTTACCAAAGCAGCCGGGTCA GTGCTTTCTCACGTTGCCGTATT CTTCAGGT 247 ITGAE NM_002208.4  3406- CTGAATGCAGAGAACCACAGAA  3505 CTAAGATCACTGTCGTCTTCCTG AAAGATGAGAAGTACCATTCTTT GCCTATCATCATTAAAGGCAGCG TTGGTGGAC 248 ITGAL NM_002209.2  3906- GTGAGGGCTTGTCATTACCAGAC  4005 GGTTCACCAGCCTCTCTTGGTTT CCTTCCTTGGAAGAGAATGTCTG ATCTAAATGTGGAGAAACTGTAG TCTCAGGA 249 JAK1 NM_002227.1   286- GAGAACACCAAGCTCTGGTATGC   385 TCCAAATCGCACCATCACCGTTG ATGACAAGATGTCCCTCCGGCTC CACTACCGGATGAGGTTCTATTT CACCAATT 250 KIAA NM_015443.3  4402- CCTTCACATCCAGATCCCTGTCG 1267  4501 GTGTTAGTTCCACTCTTGGTCTT TCACGCTCCCCTTGCCTGTGGAA CATTGTCTGGTCCTAGCTGTGGT TCCCATTG 251 MYST4 NM_012330.3  6541- CCCAGACTGTAGCCATGCAGGGT  6640 CCTGCACGGACTTTAACGATGCA AAGAGGCATGAACATGAGTGTG AACCTGATGCCAGCGCCAGCCTA CAATGTCAA 252 KCTD12 NM_138444.3  4208- ACAAGTAAAATAACTTGACATG  4307 AGCACCTTTAGATCCCTTCCCCT CCATGGGCTTTGGGCCACAGAAT GAACCTTTGAGGCCTGTAAAGTG GATTGTAAT 253 KIAA NM_014736.4   236- CGACATCAGTTTCATCGAGGAAA 0101   335 GCTGAAAATAAATATGCAGGAG GGAACCCCGTTTGCGTGCGCCCA ACTCCCAAGTGGCAAAAAGGAA TTGGAGAATT 254 SETD1B XM_037523.11  7779- ATCGTGCCCAGTGTTAACCTCGG  7878 CTGGCCTTCACTAAGGGGACTAG ACCTCCCTCTCCCCAGGAGCCCC AGCCCCAGAGTGGTTTGCAATAA TCAAGATA 255 KIR2DL XM_00112635   265- GAGGTGACATATGCACAGTTGG 5A 4.1   364 ATCACTGCGTTTTCACACAGACA AAAATCACTTCCCCTTCTCAGAG GCCCAAGACACCTCCAACAGAT ACCACCATGT 256 KIR_  NM_014512.1   719- TCCGAAACCGGTAACCCCAGAC Acti-   818 ACCTACATGTTCTGATTGGGACC vatin TCAGTGGTCAAAATCCCTTTCAC g_Sub- CATCCTCCTCTTCTTTCTCCTTC group_ ATCGCTGGT 2 257 KIR2D NM_012313.1     1- CCGGCAGCACCATGTCGCTCATG S3   100 GTCATCAGCATGGCATGTGTTGG GTTCTTCTGGCTGCAGGGGGCCT GGCCACATGAGGGATTCCGCAG AAAACCTTC 258 KLRB1 NM_002258.2   357- CAGCAACTCCGAGAGAAATGCTT   456 GTTATTTTCTCACACTGTCAACC CTTGGAATAACAGTCTAGCTGAT TGTTCCACCAAAGAATCCAGCCT GCTGCTTA 259 KLRC1 NM_002259.3   336- ACCTATCACTGCAAAGATTTACC   435 ATCAGCTCCAGAGAAGCTCATTG TTGGGATCCTGGGAATTATCTGT CTTATCTTAATGGCCTCTGTGGT AACGATAG 260 KLRC2 NM_002260.3     943- TATGTGAGTCAGCTTATAGGAAG  1042 TACCAAGAACAGTCAAACCCAT GGAGACAGAAAGTAGAATAGTG GTTGCCAATGTCTCAGGGAGGTT GAAATAGGAG 261 KLRD1 NM_002262.3   597- CAATTTTACTGGATTGGACTCTC   696 TTACAGTGAGGAGCACACCGCCT GGTTGTGGGAGAATGGCTCTGCA CTCTCCCAGTATCTATTTCCATC ATTTGAAA 262 KLRF1 NM_016523.1   544- TATACAGAAAAACCTAAGACAA   643 TTAAACTACGTATGGATTGGGCT TAACTTTACCTCCTTGAAAATGA CATGGACTTGGGTGGATGGTTCT CCAATAGAT 263 KLRF1b NM_016523.2   849- AAGTGCAATTAAATGCCAAAATC   948 TCTTCTCCCTTCTCCCTCCATCA TCGACACTGGTCTAGCCTCAGAG TAACCCCTGTTAACAAACTAAAA TGTACACT 264 KRTAP NM_198696.2   213- CTGCTGCCAGGCGGCCTGTGAGC 10-3   312 CCAGCCCCTGCCAGTCAGGCTGC ACCAGCTCCTGCACGCCCTCGTG CTGCCAGCAGTCTAGCTGCCAGC CAGCTTGC 265 KYNU NM_00103299   936- TTGCCTGCTGGTGTTCCTACAAG 8.1  1035 TATTTAAATGCAGGAGCAGGAG GAATTGCTGGTGCCTTCATTCAT GAAAAGCATGCCCATACGATTA AACCTGCGAG 266 LAMA5 NM_005560.4 11163- CCAACCCCGGCCCCTGGTCAGGC 11262 CCCTGCAGCTGCCTCACACCGCC CCTTGTGCTCGCCTCATAGGTGT CTATTTGGACTCTAAGCTCTACG GGTGACAG 267 LDHA NM_00116541  1348- ATCTTGTGTAGTCTTCAACTGGT 6.1  1447 TAGTGTGAAATAGTTCTGCCACC TCTGACGCACCACTGCCAATGCT GTACGTACTGCATTTGCCCCTTG AGCCAGGT 268 LEF1 NM_016269.4  3136- AACACATAGTGGCTTCTCCGCCC  3235 TTGTAAGGTGTTCAGTAGAGCTA AATAAATGTAATAGCCAAACCC ACTCTGTTGGTAGCAATTGGCAG CCCTATTTC 269 LETM2 NM_144652.3  1331- AAAGGACCCATCACTTCTTCTGA  1430 AGAACCTACACTCCAGGCCAAAT CACAAATGACGGCCCAGAACAG CAAGGCTAGTTCAAAAGGAGCA TAAAGGACTA 270 LIF NM_002309.3  1241- GGGATGGAAGGCTGTCTTCTTTT  1340 GAGGATGATCAGAGAACTTGGG CATAGGAACAATCTGGCAGAAG TTTCCAGAAGGAGGTCACTTGGC ATTCAGGCTC 271 LILRA5 NM_021250.3  1044- TTGAATGCTGGAGCCTTGGAAGC  1143 GAATCTGATGGTCCTAGGAGGTT CGGGAAGACCATCTGAGGCCTAT GCCATCTGGACTGTCTGCTGGCA ATTTCTTT 272 LILRA5  NM_181879.2   546- CACCCTCTCAGCCCTGCCCAGTC b   645 CTGTGGTGACCTCAGGAGAGAA CGTGACCCTCCAGTGTGGCTCAC GGCTGAGATTCGACAGGTTCATT CTGACTGAG 273 LOC NR_002809.2   471- GCGGCAGCCAATCAGCGCGCGG 338799   570 CTTCTATAGGGCTTGAGTTATTA GACGCTGATCTCAAAACATCCTT CATCAGACACGAAGGAGAGGCC AACAGATGAG 274 LOC100 XM_00171659   568- AGGGTCATGCAGCTACTGAGGTC 129022 1.1   667 ACAGCCTGGATTCATACACAGGT CTGACTCCTGAGCACTTAGCCAG GTGGCTGTAACAGTGTTCCCAGA AACACAGG 275 LOC100 XM_00173282  1148- ACCTGTCTTCCGGGTCTGTTCAC 129697 2.2  1247 CCGTCCCCTGGACTGGCACCAGC ACAGAGGGTCGAGTGTTGGCAC CTGTCTTCTGGGTCTCCATCCCT CCCTTTGTT 276 LOC100 XM_00171715  1469- GAGAATGTCTGCGCGGAGACAG 130229 8.1  1568 CATAGCTCTGTAGAAATGAGTGG CAGCGTATGTAACCTGGCATTTT GAACCCAGGAGCACAATTTTATT AAAGGAAAA 277 LOC100 XR_036994.1    15- GAGTAGTAGGTGGACAGCCGTC 132797   114 CCACACAAGGGTTTGTATCTGGG CTACACAGATTCCCTTCAGAAAA GCACCAATGTAAGCAACTCCCTT ACAGTTGCT 278 LOC100 XR_039238.1   342- GAGATAGCTTCCTGAAATGTGTG 133273   441 AAGGAAAATGATCAGAAAAAGA AAGAAGCCAAAGAGAAAGGTAC CTGGGTTCAACTAAAGTGCCAGC CTGCTCCACC 279 LOC NM_144692.1  3367- GCTCTGTCCTTTGCCGCTCAGAC 148137  3466 CAAAAACCTTAGAGCTGTCTTTG ACTTCTGTCTTTCCCTTCCACCC ACAGTTAACCAGGAAATCCTGCC ATCTCCGC 280 LOC NR_024275.1  5062- GGTTACAGCCATTTTGTGTGATT 151162  5161 CACTTCGGGGGTTAAGTAATGCA GGATTCTGCAAACAAGGTGTCGC CGTCCAAATGTACTGTCCTGGCA TAGAGAGC 281 Clorf NM_00100380  2561- ACATGGCGCCACGGCCACTTCCT 222 8.1  2660 GCTGCCCTGGACCCCGCAAGCCC AGGGACATCCAAGAGCACCCCT CCTGAGACCCCAGACTCAGAAG CAGCGAGAAG 282 LOC XM_934917.1   376- CCCCTGGTGGACCGCGACCTCCG 339674   475 CAAGACGCTAATGGTGCGCGAC AACCTGGCCTTCGGCGGCCCGGA GGTCTGAGCCGACTTGCAAAGG GGATAGGCGG 283 LOC XM_371757.4   210- GCAAAGCACTATCACAAGGAAT 648000   309 ATAGGCAAATGTACAGAACTGA AATTCGAGTGGCGAGGATGGCA AGAAAAGCTGGCAACTTCTATGT ACCTGCAGAAC 284 LOC XR_017684.2    82- AAGATTATGTCTTCCCCTGTTTC 391126   181 CAAAGAGCTGAGACAGAAGTACA ATGTGCAATCCATGCCCATCCGA AAGGATGATGAAGTTCAGGTTGT ACGAGGGC 285 LOC XM_930634.1  1448- ATGGGACCCACTCTACTGAGGCT 399753  1547 TTATGTAGAACTCATAGAGGAAG CTGGCTTTGAGGAATGAACTACC CTGTGCTTTTCTTAGGACTAAAA TCTCAGGA 286 LOC XM_934471.1    21- GACGGTAACCGGGACCCAGTGT 399942   120 CTGCTCCTGTCACCTTCGCCTCC TAATCCCTAGCCACTATGCGAGA TGACTCCTTCAACACCTTCAGTG AGACGGGTG 287 LOC XM_498648.3   552- GAGTTTTCCAAACCCTGGATTTC 440389   651 CTTCGGAGAGAGCTAGATTCTAT TCCATTCTTGGAATTCAGCTCCT TGCCCTTCTCTGTGACCCCGGAT CGCGAATG 288 LOC XM_942885.1  1533- TGTTGCAAAAGCCAACTACCACT 440928  1632 GTCAAACTTAGCCCGTTTACAAC ATGGGGAAAGGCGTATTTCTTAC TAATATCTCAACAACGATAACAA TGCTGTAT 289 LOC XR_018937.2   287- CGGGTGCAGCGGGAAAAGGCTA 441073   386 ATGGCACAACTGTCCACGTAGGC ATTCACCCCAGCAAGGTGGTTAT CACTAGGCTAAAACTGGACAAA GACTGTGAAA 290 LOC XR_036892.1   591- GGTGAAGAATTTGTTCTATTATG 642812   690 AAGATACTGTCTGGGCTAAAAA GCTTACAGTGAGTGGAAGATAG CAACTTGTAGGGTTGGTGGCTGA ACAGGCCGAC 291 LOC XM_927980.1   255- CTGGCTCAAGGATGGCACGGTGT 643319   354 TATGTGAGCTCAATAATGCACTG TACCCCAAGGGGCAGGTCCCAGT AAAGAAGATCCAGGCCTCCACC ATGGCCTTC 292 LOC XR_017529.2    38- CAGGCGCTGCAAGTTCTCCCAGG 644315   137 AGAAAGCCATGTTCAGTTCGAGC GCCAAGATCGTGAAGCCCAATG GCGAGAAGCCGGACGAGTTCGA GTCCGGCCAT 293 LOC XM_928884.1    13- GAAGCACTGGTAAATGTCTGCTG 645914   112 CATTAACTCACTCAGACCAAACT TTCTCTTATCTAGGTCCAAAAGG AAGCTGCTCGGCTGGAAGGAAC CTGGTGAGG 294 LOC XR_018104.1   670- AGGTGCTGCAAAATTACCAGGA 647340   769 ATACAGTCTGGCCAACAGCATCT ACTACTCTCTGAAGGAGTCCACC ACTAGTGAGCAGAGTGCCAGGA TGACAGCCAT 295 LOC XR_038906.2  1638- TGGAGAGAAGAATGAAGAGGTG 648927  1737 GTGGTTCTGGGTTTGATTTGAGT TCACCTGTGGGCAGTGGGCAGTG TCTTGGTGAAAGGGAGCGGATA CTACTTTTTG 296 LOC XM_938755.1    38- GCCCTTCTGCCATCAACGAGGTG 653773   137 GTGACCCAAGAACATACCATCA ACATTCACAAGCGCATCCATGGA GAGGGCTTCAAGAAGCGTGCTCC TCGGGCACT 297 LOC XR_015610.3  1861- GTAGTTGTCCACTGCTTTCCTGG 728533  1960 ATGGATGGGACTCTTATGTCATA ACTTCTATACTCCTTTGGCCCAT AGCTAAGGTCATCCTTCCCCACA GGGGTGGC 298 LOC XM_00113319   510- CCAAACCAAAAGAGGCAAGCAA 728835 0.1   609 GTCTGCGCTGACCCCAGTGAGTC CTGGGTCCAGGAGTACGTGTATG ACCTGGAACTGAACTGAGCTGCT CAGAGACAG 299 LOC XR_040891.2   625- CCCTGGGTGCCCCTTAACCCGGG 729887   724 CGGTAGCTCGTTAAGATGGCGAA GTGTCCGGTCCGGAACACGCGA AACCCCAAATCCCGCCTGCCCGA CCTCCTGAC 300 LOC XM_00113427   765- GCGCGGTTGCGGTTAGCGGGCGC 732111 5.1   864 GGTGCCAAAGCTGCCATCCCCAG CTCACAGCTCCTCATATCCACCC TGCCCTCATCTTTATGAATTGCG TGTAGACC 301 LOC XM_00113301   182- GCCCTTCAGAGCTGCGGGAGATC 732371 9.1   281 ATTGATGAGTGCCGGGCCCATGA TCCCTCTGTGCGGCCCTCTGTGG ATGAGCAGAAGCGCAGACTTAA TGATGTGTT 302 LOC NM_00109977  2666- ATGTTGCATTGACTAGAGGAAAG 91431 6.1  2765 AGGCATTTGTTGATTGTGGGAAA TTTAGCCTGTTTGAGGAAAAATC AACTTTGGGGACGAGTGATCCAA CACTGCGA 303 P2RY5 NM_005767.5  2026- AGATTGTTTGCACTGGCGTGTGG  2125 TTAACTGTGATCGGAGGAAGTGC ACCCGCCGTTTTTGTTCAGTCTA CCCACTCTCAGGGTAACAATGCC TCAGAAGC 304 LPCAT4 NM_153613.2  1560- CCCCACACACCTCTCGAGGCACC  1659 TCCCAGACACCAAATGCCTCATC CCCAGGCAACCCCACTGCTCTGG CCAATGGGACTGTGCAAGCACCC AAGCAGAA 305 LPIN2 NM_014646.2  5620- AGAAAAAACTTAAAAATGGGAT  5719 GTCCTAAAATGAAAGCTGCTCAA AGTCACAGAACAACCGAGGGAC AAAGGAGATTGGATGACTGGGA AGCGCTGGCCC 306 Clorf NM_018372.3  1543- TTCCAATACCCAGCTTGCTTCCA 103  1642 TGGCCAATCTAAGGGCAGAGAA GAATAAAGTGGAGAAACCATCT CCTTCTACCACAAATCCACATAT GAACCAATCC 307 LRRC47 NM_020710.2  2461- GGGTCAGTGACGGACACTTACCT  2560 GACAGCGGATCCACAATATTCTC GTGCAGTGTGTTTGGAATCCTGG TCTGGGCTCTCGTCGTTGGCCTT GTAGATCA 308 LY96 NM_015364.4   439- AAGGGAGAGACTGTGAATACAA   538 CAATATCATTCTCCTTCAAGGGA ATAAAATTTTCTAAGGGAAAATA CAAATGTGTTGTTGAAGCTATTT CTGGGAGCC 309 LYN NM_002350.1  1286- TCCTGAAGAGCGATGAAGGTGG  1385 CAAAGTGCTGCTTCCAAAGCTCA TTGACTTTTCTGCTCAGATTGCA GAGGGAATGGCATACATCGAGC GGAAGAACTA 310 MAGEA1 NM_004988.4   477- AGGGGCCAAGCACCTCTTGTATC   576 CTGGAGTCCTTGTTCCGAGCAGT AATCACTAAGAAGGTGGCTGATT TGGTTGGTTTTCTGCTCCTCAAA TATCGAGC 311 MAGEA3 NM_005362.3   850- ACTGTGCCCCTGAGGAGAAAATC   949 TGGGAGGAGCTGAGTGTGTTAG AGGTGTTTGAGGGGAGGGAAGA CAGTATCTTGGGGGATCCCAAGA AGCTGCTCAC 312 MAP3K7 NM_145333.1   671- GCCATATTATACTGCTGCCCACG   770 CAATGAGTTGGTGTTTACAGTGT TCCCAAGGAGTGGCTTATCTTCA CAGCATGCAACCCAAAGCGCTA ATTCACAGG 313 MARCKS NM_002356.6  1800- GTCAAAAAGGGATATCAAATGA  1899 AGTGATGGGGTCACAATGGGGA AATTGAAGTGGTGCATAACATTG CCAAAATAGTGTGCCACTAGAA ATGGTGTAAAG 314 MARCKS NM_023009.5  1117- TCCAAGTAGGTTTTGTTTACCCT L1  1216 ACTCCCCAAATCCCTGAGCCAGA AGTGGGGTGCTTATACTCCCAAA CCTTGAGTGTCCAGCCTTCCCCT GTTGTTTT 315 MBD1 NM_015844.2  2380- TGGCTGCAGGCCTGACTACTGCC  2479 CACACCAACGAGGTGATCTAGC AGATACATGGCAACGTGTGAACT GCAACAACGCCTGGTGCCCCAGC ACCAACCTT 316 C19orf NM_174918.2  1062- CATACTAGAGTATACTGCGGCGT 59  1161 GTTTTCTGTCTACCCATGTCATG GTGGGGGAGATTTATCTCCGTAC ATGTGGGTGTCGCCATGTGTGCC CTGTCACT 317 MED16 NM_005481.2  2152- TCTGAAGCCCAGCTGCCTGCCCG  2251 TGTATACGGCCACCTCGGATACC CAGGACAGCATGTCCCTGCTCTT CCGCCTGCTCACCAAGCTCTGGA TCTGCTGT 318 MEN1 NM_130799.2  2222- CCCAGCCCCTAGAAACCCAAGCT  2321 CCTCCTCGGAACCGCTCACCTAG AGCCAGACCAACGTTACTCAGG GCTCCTCCCAGCTTGTAGGAGCT GAGGTTTCA 319 MERTK NM_006343.2   666- GAAGAGATCGTGTCTGATCCCAT   765 CTACATCGAAGTACAAGGACTTC CTCACTTTACTAAGCAGCCTGAG AGCATGAATGTCACCAGAAACA CAGCCTTCA 320 MFSD1 NM_022736.2  2023- AAGGGCTGCGTTACACAAAATA  2122 AACAATGGCATTGTCATAGGCCT TCCTTTTACTAGTAGGGCATAAT GCTAGGGAATATGTGAAGATGTT TTTATGAAG 321 MID1IP NM_021242.5  3472- AGCTGGCATTTCGCCAGCTTGTA 1  3571 CGTAGCTTGCCACTCAGTGAAAA TAATAACATTATTATGAGAAAGT GGACTTAACCGAAATGGAACCA ACTGACATT 322 MPDU1 NM_004870.3  1226- CATTCAGCCAAGCCTCCTCCTCT  1325 AGCAGCAATTTCCAGCTGTGTAA CACTATCCTGGGCAAATGTTTTA CCCTGTCCTCCAGCCTCCCTGCT TCCCTTCT 323 MRPL27 NM_148571.1  2189- TCAAACTGGTAGCTATGCTTTGA  2288 TGTCCTGTTGAGGCCATCGGACA GAGACTGGAGCCCAGGTGACAG GAGATGGTGATACCAGAAGTCA AGGGTTGGGG 324 MRPS16 NM_016065.3  1811- ATTCAAATGTGGCTGTGATTTCT  1910 GCATATATCATAGATGGGATCCT TCTGAGAATACTGGAATAGGGA ATTAGGACACCAAGCCAATTCAG CTGTGAACC 325 MS4A2 NM_000139.3   662- TTCTCACCATTCTGGGACTTGGT   761 AGTGCTGTGTCACTCACAATCTG TGGAGCTGGGGAAGAACTCAAA GGAAACAAGGTTCCAGAGGATC GTGTTTATGA 326 MS4A6A NM_022349.3  1290- CTGGGAAGTTAAATGACTGGCCT  1389 GGCATTATGCTATGAGTTTGTGC CTTTGCTGAGGACACTAGAACCT GGCTTGCCTCCCTTATAAGCAGA AACAATTT 327 MS4A6A  NM_152851.2   880- CTGCGGTGGAAACAGGCTTACTC b   979 TGACTTCCCTGGGAGTGTACTTT TCCTGCCTCACAGTTACATTGGT AATTCTGGCATGTCCTCAAAAAT GACTCATG 328 MTCH1 NM_014341.2  2081- TCCTCCTCATCTAATGCTCATCT  2180 GTTTAATGGTGATGCCTCGCGTA CAGGATCTGGTTACCTGTGCAGT TGTGAATACCCAGAGGTTGGGCA GATCAGTG 329 MYADM NM_00102082  2656- TCTTTTTCCTGGCCATGAGGACA 0.1  2755 AAAATTACTGAGTGGCCCTTAAA GAGGGAAGTTTGTTTTCAGCTGT TCTCTTTTGCCCGTAGGTGGGAG GGTGGGGA 330 MYADM  NM_00102082  2789- TGAATGTGTAGTGCACACGCACG b 0.1  2888 GGTGTTTCTGTGTGCTAGTTGCT TCTTGCTGCTGCTTCCTGCTTGT CTGGGACTCACATACATAACGTG ATATATAT 331 C19orf NM_019107.3   649- TGTCCCTGAAAGGGCCAGCACAT 10   748 CACTGGTTTTCTAGGAGGGACTC TTAAGTTTTCTACCTGGGCTGAC GTTGCCTTGTCCGGAGGGGCTTG CAGGGTGG 332 MYL12A NM_006471.3   305- TCTCTGGGTAGCAGGGTGGTGTG   404 ATAGCGGCAGCGAGGGGCTCGG AGAGGTGCTCGGATTCTCGTAGC TGTGCCGGGACTTAACCACCACC ATGTCGAGC 333 MYLIP NM_013262.3  2701- TTGGGCATTTTGGAAGCTGGTCA  2800 GCTAGCAGGTTTTCTGGGATGTC GGGAGACCTAGATGACCTTATCG GGTGCAATACTAGCTAAGGTAA AGCTAGAAA 334 NAT5 NM_181528.3   735- AAACATACCACTCTCATGGTTCA   834 TAGTATTCACTGTATGTATGCTA GGGAAAAGACTTGCTCCAGTCTC CTCCTCAGTTCTGTGCCTGAGAA CCACTGCT 335 NADK NM_023018.4  2449- TCCGGGGCTAGTGATCGTGATCC  2548 CTTTTATTTGCAACTGTAATGAG AATTTTTCACACTAACACAGCGA GGGACTCAACACGCTGATTCTCC TCCTGCCT 336 NAGK NM_017567.4  1362- GGGCCAGGCACATCGGGCACCT  1461 CCTCCCCATGGACTATAGCGCCA ATGCCATTGCCTTCTATTCCTAC ACCTTTTCCTAGGGGGCTGGTCC CGGCTCCAC 337 NCAPG NM_022346.4  3080- ACCCAAGCATCAAAGTCTACTCA  3179 GCTAAAGACTAACAGAGGACAG AGAAAAGTGACAGTTTCAGCTA GGACGAACAGGAGGTGTCAGAC TGCTGAAGCCG 338 NCOA5 NM_020967.2  2837- TGGACATGTTCTCGAGATGGGTG  2936 GCTGTTCGCGACTTTTGTACCAG AGTGAAATTGTTAGAAGGAGGG TTTCTGGCTGTGGTTCTAAATGG AGCCCCAGG 339 NCR1 NM_004829.5   603- CGATGTTTTGGCTCCTATAACAA   702 CCATGCCTGGTCTTTCCCCAGTG AGCCAGTGAAGCTCCTGGTCACA GGCGACATTGAGAACACCAGCC TTGCACCTG 340 NDRG2 NM_016250.2  1516- TATGCATCCTCTGTCCTGATCTA  1615 GGTGTCTATAGCTGAGGGGTAAG AGGTTGTTGTAGTTGTCCTGGTG CCTCCATCAGACTCTCCCTACTT GTCCCATA 341 NDUFA4 NM_002489.3   262- TGGGACAGAAATAACCCAGAGC   361 CCTGGAACAAACTGGGTCCCAAT GATCAATACAAGTTCTACTCAGT GAATGTGGATTACAGCAAGCTG AAGAAGGAAC 342 NDUFAF NM_174889.4   486- TCCTGCCTCCACCAGTTCAAACT 2   585 CAAATTAAAGGCCATGCCTCTGC TCCATACTTTGGAAAGGAAGAAC CCTCAGTGGCTCCCAGCAGCACT GGTAAAAC 343 NDUFB3 NM_002491.2   383- ACAATGGAAGATAGAAGGGACA   482 CCATTAGAAACTATCCAGAAGA AGCTGGCTGCAAAAGGGCTAAG GGATCCATGGGGCCGCAATGAA GCTTGGAGATAC 344 NDUFS4 NM_002495.2   326- GAGTTTGATACCAGAGAGCGAT   425 GGGAAAATCCTTTGATGGGTTGG GCATCAACGGCTGATCCCTTATC CAACATGGTTCTAACCTTCAGTA CTAAAGAAG 345 NDUFV2 NM_021074.4   687- TTACTATGAGGATTTGACAGCTA   786 AGGATATTGAAGAAATTATTGAT GAGCTCAAGGCTGGCAAAATCC CAAAACCAGGGCCAAGGAGTGG ACGCTTCTCT 346 NFAT5 NM_138713.3  3857- CCCAAGAAGCATTTTTTGCAGCA  3956 CCGAACTCAATTTCTCCACTTCA GTCAACATCAAACAGTGAACAA CAAGCTGCTTTCCAACAGCAAGC TCCAATATC 347 NFATC1 NM_172389.1  1985- CGAATTCTCTGGTGGTTGAGATC  2084 CCGCCATTTCGGAATCAGAGGAT AACCAGCCCCGTTCACGTCAGTT TCTACGTCTGCAACGGGAAGAG AAAGCGAAG 348 NFATC4 NM_00113602  2297- ACAAGAGGGTTTCCCGGCCAGTC 2.2  2396 CAGGTCTACTTTTATGTCTCCAA TGGGCGGAGGAAACGCAGTCCT ACCCAGAGTTTCAGGTTTCTGCC TGTGATCTG 349 NFKB1 NM_003998.3  3606- CGGATGCATCTGGGGATGAGGTT  3705 GCTTACTAAGCTTTGCCAGCTGC TGCTGGATCACAGCTGCTTTCTG TTGTCATTGCTGTTGTCCCTCTG CTACGTTC 350 NFKB2 NM_002502.2   826- ATCTCCGGGGGCATCAAACCTGA   925 AGATTTCTCGAATGGACAAGACA GCAGGCTCTGTGCGGGGTGGAG ATGAAGTTTATCTGCTTTGTGAC AAGGTGCAG 351 NIPBL NM_133433.3  8755- GCGCCGTGATGGCCGCAAACTG  8854 GTGCCTTGGGTAGACACTATTAA AGAGTCAGACATTATTTACAAAA AAATTGCTCTAACGAGTGCTAAT AAGCTGACT 352 NLRP3 NM_00107982   416- AGTGGGGTTCAGATAATGCACGT 1.2   515 GTTTCGAATCCCACTGTGATATG CCAGGAAGACAGCATTGAAGAG GAGTGGATGGGTTTACTGGAGTA CCTTTCGAG 353 NME1- NM_00101813   484- ACCTGGAGCGCACCTTCATCGCC NME2 6.2   583 ATCAAGCCGGACGGCGTGCAGC GCGGCCTGGTGGGCGAGATCATC AAGCGCTTCGAGCAGAAGGGAT TCCGCCTCGT 354 NUDT18 NM_024815.3  1369- CCCCAGTGGCATCTCCTCATCAC  1468 GTTCTGTGCCGTCCTTGGGAAAG GCCTGCATTCTGATCCTTCCAGG CCCTTCGAGCATGGAGGGGCACT GGGGAAGG 355 NUMB NM_00100574  2833- CATAAGATTGATTTATCATTGAT 4.1  2932 GCCTACTGAAATAAAAAGAGGA AAGGCTGGAAGCTGCAGACAGG ATCCCTAGCTTGTTTTCTGTCAG TCATTCATTG 356 NUP153 NM_005124.3  5104- TTTATGATCCAGCAGATTATTCA  5203 CTGATTTGACATAGTCTGGCTGT ACCCAGGAATGGAGCCTGCACG GTGAATGGCTTTGTATAGAACCT CTTTGTCTA 357 OLR1 NM_002543.3  1524- ACACATTTTGGGACAAGTGGGG  1623 AGCCCAAGAAAGTAATTAGTAA GTGAGTGGTCTTTTCTGTAAGCT AATCCACAACCTGTTACCACTTC CTGAATCAGT 358 OSBP ILMN 170637   130- TTCTCTTCCTTCACCATCTGCAC 6.1   229 TACATTTCTGGCTGATCCCAATC AGATTCCCGCTAATGGAAGAAGT TTAGAATCTTTCAGGTGGAATAA AGTCACAT 359 FAM105 NM_138348.4  2537- TGCAGATGGTGTTCACATGAACC B  2636 GGAGACATCACTCTTTAGGATTC TACTGGCAGCCCCTGAATTGGCT CAACGTTIGTGGAGGTGGTATTT CCCTGAAG 360 P2RY10 NM_198333.1   972- TTACACCATGGTAAAGGAAACC  1071 ATCATTAGCAGTTGTCCCGTTGT CCGAATCGCACTGTATTTCCACC CTTTTTGCCTGTGCCTTGCAAGT CTCTGCTGC 361 PACS1 NM_018026.3  3830- CGCTGTCTTCGTGGCTTCCACCC  3929 TTGTTAATGATGCTCCTGCCTCT GCCTCCCAGCCCCTCACCCAGCA CAGCTCTGCCTGGACTTGGAGAG ATGGGAGG 362 PANK2 NM_153640.2   824- AGTGGATAAACTAGTACGAGAT   923 ATTTATGGAGGGGACTATGAGA GGTTTGGACTGCCAGGCTGGGCT GTGGCTTCAAGCTTTGGAAACAT GATGAGCAAG 363 PDCD10 NM_145859.1   901- AAGAGATGTACTTCTCAGTGGCA  1000 GTATTGAACTGCCTTTATCTGTA AATTTTAAAGTTTGACTGTATAA ATTATCAGTCCCTCCTGAAGGGA TCTAATCC 364 PDGFD NM_033135.3  3394- CCTGTGAAAACATCAGTTTCCTG  3493 TACCAAAGTCAAAATGAACGTTA CATCACTCTAACCTGAACAGCTC ACAATGTAGCTGTAAATATAAAA AATGAGAG 365 PDSS1 NM_014317.3  1199- CATGAAGCAATAAGAGAGATCA  1298 GTAAACTTCGACCATCCCCAGAA AGAGATGCCCTCATTCAGCTTTC AGAAATTGTACTCACAAGAGAT AAATGACAAC 366 PELP1 NM_014389.2  1989- TGGCCCCGTCTCCTCGCTGCCCA  2088 CCTCCTCTTGCCTGTGCCCTGCA AGCCTTCTCCCTCGGCCAGCGAG AAGATAGCCTTGAGGTCTCCTCT TTCTGCTC 367 PFAS NM_012393.2  5109- CATCCCTAGATCCTAACCCTTTA  5208 GTATGCTGGAATTCTACTCTTCA CTTACTGCATTGACTGTTGTTGA TTAGTTATTATTGCAAAGCACTG TCACCGGC 368 PFDN5 NM_145897.2   232- ATCGATGTGGGAACTGGGTACTA   331 TGTAGAGAAGACAGCTGAGGAT GCCAAGGACTTCTTCAAGAGGA AGATAGATTTTCTAACCAAGCAG ATGGAGAAAA 369 PFDN5  NM_145897.2   331- ATCCAACCAGCTCTTCAGGAGAA b   430 GCACGCCATGAAACAGGCCGTC ATGGAAATGATGAGTCAGAAGA TTCAGCAGCTCACAGCCCTGGGG GCAGCTCAGG 370 PGK1 NM_000291.3  1122- GTCCTGAAAGCAGCAAGAAGTA  1221 TGCTGAGGCTGTCACTCGGGCTA AGCAGATTGTGTGGAATGGTCCT GTGGGGGTATTTGAATGGGAAG CTTTTGCCCG 371 PHF8 NM_015107.2  5704- ATCAAGGTTTAGAACACCATGAG  5803 ATAGTTACCCCTGATCTCCAGTC CCTAGCTGGGGGCTGGACAGGG GGAAGGGAGAGAGGATTTCTAT TCACCTTTAA 372 PHLPP2 NM_015020.3  7601- CCAGTTGGGTGTGGCAGATCTAC  7700 TGAATATCAAATGATGCTCTTCT TCCCATGTAGACCTTCAGCAAAA GCCGGTACTTGGAAGCCACAGG CTCACCTTC 373 PHRF1 NM_020901.3  5239- GGGAAATGGGGGGCATCACCAT  5338 GCCTGCCGTCGGGTTCCTGCGCT GACACCTGGTCTGTGCACCTGTG TTGCTCACAGTTGAAAACTGGAC ACTTTTGTA 374 PI4K2A NM_018425.3  3886- TCCATGGAATTGCTGAGACGTGG  3985 CTCCTGGGGCTATTTCTCCCTAA TAAAGGATGATCCAGGTCCTCAT TTCCAAAGTCCCAATGCTCTGAA AACCAAAA 375 PIK3CD NM_005026.3  4799- GAGCCAGAAGTAGCCGCCCGCT  4898 CAGCGGCTCAGGTGCCAGCTCTG TTCTGATTCACCAGGGGTCCGTC AGTAGTCATTGCCACCCGCGGGG CACCTCCCT 376 PIM2 NM_006875.3  1947- TTTTTGGGGGATGGGCTAGGGGA  2046 AATAAGGCTTGCTGTTTGTTCTC CTGGGGCGCTCCCTCCAACTTTT GCAGATTCTTGCAACCTCCTCCT GAGCCGGG 377 PLAC8 NM_00113071   289- CTGATATGAATGAATGCTGTCTG 5.1   388 TGTGGAACAAGCGTCGCAATGA GGACTCTCTACAGGACCCGATAT GGCATCCCTGGATCTATTTGTGA TGACTATAT 378 PLEKHG NM_015432.3  6365- CCAGTIGTGGGTTAAGAATAGGC 4  6464 TAGAGCAGACATTGGGTGTTTCC ATGCTGTAGGCTGGTGGGGGACC ATGTGCCTCTAGGCAGTGACTAG GGTGCCCC 379 POLR2A NM_000937.4  6539- CCCCTGCCTGTCCCCAAATTGAA  6638 GATCCTTCCTTGCCTGTGGCTTG ATGCGGGGCGGGTAAAGGGTAT TTTAACTTAGGGGTAGTTCCTGC TGTGAGTGG 380 PPP1R XM_927029.1  4342- CAGAACCTCCTCAGTTCCTTCAC 3E  4441 AGTGCAACCCTGTGTACTTGGCC CGCAACCCAATAGTATTGTGCCT CACTTCACCTTCCATGGGCAACT GCCCTCCC 381 PPP2R NM_178588.1   941- ACAGCACCCTCACGGAACCAGT 5C  1040 GGTGATGGCACTTCTCAAATACT GGCCAAAGACTCACAGTCCAAA AGAAGTAATGTTCTTAAACGAAT TAGAAGAGAT 382 PPP6C NM_002721.4  1536- TTAAGAAATTTCAGCAGCAAAGT  1635 TGTTATTCAGTGGGCACGATGGA CTCCAAATGCCTCAAGTTATGTA TACCTGTCCCAGATGTAAACTTC ATTGTCCT 383 PRG2 NM_002728.4   257- CTCTGGAAGTGAAGATGCCTCCA   356 AGAAAGATGGGGCTGTTGAGTCT ATCTCAGTGCCAGATATGGTGGA CAAAAACCTTACGTGTCCTGAGG AAGAGGAC 384 PRPF3 NM_004698.2  2116- CCTACAGAGAACATGGCTCGTGA  2215 GCATTTCAAAAAGCATGGGGCTG AACACTACTGGGACCTTGCGCTG AGTGAATCTGTGTTAGAGTCCAC TGATTGAG 385 PRPF8 NM_006445.3  7091- ACTCTGCGGATCGGGAGGACCTG  7190 TATGCCTGACCGTTTCCCTGCCT CCTGCTTCAGCCTCCCGAGGCCG AAGCCTCAGCCCCTCCAGACAGG CCGCTGAC 386 C22orf NM_173566.2 10495- CCCGTTGAGCTGGCCATCTAGTG 30 10594 CAGTGTGCTCTCAGATTCCATGT TTGTTGATTGTGTGTCTTCACAA GCCCCTCTCTGGTGCTGAATTGG ATTTGAAT 387 BAT2D1 NM_015172.3  9620- AGAACAGTGAGTACCTAGAACT  9719 GTGCCACTAATTAAAGGAAATCC TAAGAAGGTGCATTTCTTTACAG AGCTGTGTCATGCCATCCTTTGG GCCCTCTGC 388 PRRG4 NM_024081.5   761- GAAGACCTGAGGAGGCTGCCTT   860 GTCTCCATTGCCGCCTTCTGTGG AGGATGCAGGATTACCTTCTTAT GAACAGGCAGTGGCGCTGACCA GAAAACACAG 389 PSMA3 NM_152132.2   422- CTTTGGCTACAACATTCCACTAA   521 AACATCTTGCAGACAGAGTGGCC ATGTATGTGCATGCATATACACT CTACAGTGCTGTTAGACCTTTTG GCTGCAGT 390 PSMA4 NM_002789.3   541- GTACATTGGCTGGGATAAGCACT   640 ATGGCTTTCAGCTCTATCAGAGT GACCCTAGTGGAAATTACGGGG GATGGAAGGCCACATGCATTGG AAATAATAGC 391 PSMA4  NM_002789.4   879- GAGGAAGAAGAAGCCAAAGCTG b   978 AGCGTGAGAAGAAAGAAAAAGA ACAGAAAGAAAAGGATAAATAG AATCAGAGATTTTATTACTCATT TGGGGCACCAT 392 PSMA6 NM_002791.2   218- GGTCGGCTCTACCAAGTAGAATA   317 TGCTTTTAAGGCTATTAACCAGG GTGGCCTTACATCAGTAGCTGTC AGAGGGAAAGACTGTGCAGTAA TTGTCACAC 393 PSMA6  NM_002791.2   866- GATGCTCACCTTGTTGCTCTAGC b   965 AGAGAGAGACTAAACATTGTCG TTAGTTTACCAGATCCGTGATGC CACTTACCTGTGTGTTTGGTAAC AACAAACCA 394 PSMB1 NM_002793.3   687- GCGGCTGGTGAAAGATGTCTTCA   786 TTTCTGCGGCTGAGAGAGATGTG TACACTGGGGACGCACTCCGGAT CTGCATAGTGACCAAAGAGGGC ATCAGGGAG 395 PSMB7 NM_002799.2   421- GTTACATTGGTGCAGCCCTAGTT   520 TTAGGGGGAGTAGATGTTACTGG ACCTCACCTCTACAGCATCTATC CTCATGGATCAACTGATAAGTTG CCTTATGT 396 PSMB8 NM_004159.4  1216- ACTCACAGAGACAGCTATTCTGG  1315 AGGCGTTGTCAATATGTACCACA TGAAGGAAGATGGTTGGGTGAA AGTAGAAAGTACAGATGTCAGT GACCTGCTGC 397 PSMC1 NM_002802.2  1487- CATCCTGTGTCTTTTGGAGTACG  1586 ATGTGTAAGTGCCCATTGGGTGG CCTGTTGGTCACTGTGCAGCAGT CTGCTTCCCAATAAAGCGTGCTC TTTCACAA 398 PSMD7 NM_002811.4  1231- GAGCTCTCTGCCTCCGGTCACTC  1330 TTGCTGTGGTGCTACGTGGAAGT GAATGGAGACTGATCTCAAATCT GAACTGCAGCTTTCGCTGCTGTG AGTTGGGG 399 PSME3 NM_005789.3  3203- TCCCGAGTGATACCCATGAACTG  3302 CCAGTAGAGGCTGCTATCGTTCC ATGTGTAAGGAATGAACTGGTTC AAGGCGCGTCCTACCCAGTCATT TTCTTTAC 400 PTGDR NM_000953.2  2341- TATGATGACTGAAAGGGAAAAG  2440 TGGAGGAAACGCAGCTGCAACT GAAGCGGAGACTCTAAACCCAG CTTGCAGGTAAGAGCTTTCACCT TTGGTAAAAGA 401 PTGDR2 NM_004778.1  1836- GCCAATGCTTACTGCGCTAGACG  1935 CTTCATCCCACAATCTTAAGGGG CAGCTTCTATTAGCCAGTCTTTA CAGCTGAGCACATTCTGGCTCAG GGAGGTTA 402 PUM1 NM_00102065  3753- AAATGTTCTAGTGTAGAGTCTGA 8.1  3852 GACGGGCAAGTGGTTGCTCCAG GATTACTCCCTCCTCCAAAAAAG GAATCAAATCCACGAGTGGAAA AGCCTTTGTA 403 QTRTD1 NM_024638.3  2508- TTAGATTAGAGTCATAGCCTTAA  2607 TAGCCCTAGTTGTCATCCTGGGA GACAGGCAACAGTAGAGATATT TGAGAGCCTAAAGAGAGGTTTG GCCTGTGGGT 404 RAB10 NM_016131.4  3593- AGGGCTTTGCCCCTTTTCTGTAA  3692 GTCTCTTGGGATCCTGTGTAGAA GCTGTTCTCATTAAACACCAAAC AGTTAAGTCCATTCTCTGGTACT AGCTACAA 405 RAG1 NM_000448.2  2301- CAGTCTACATTTGTACTCTTTGT  2400 GATGCCACCCGTCTGGAAGCCTC TCAAAATCTTGTCTTCCACTCTA TAACCAGAAGCCATGCTGAGAAC CTGGAACG 406 RASSF5 NM_182664.2  3061- TCGTCCTGCATGTCTCTAACATT  3160 AATAGAAGGCATGGCTCCTGCTG CAACCGCTGTGAATGCTGCTGAG AACCTCCCTCTATGGGGATGGCT ATTTTATT 407 RBM14 NM_006328.3  2661- TGGTATGTATCCAAGTCCCTGCT  2760 GACCACTAATGTTCTAGCTGATG GTGAGCGGCACAGTCCCACTTCC CCATCTCCCCAAGTAGGTGGTGT TAGAAAAC 408 RBM4B NM_031492.3  1557- TAGGAGTTGAATCCTTCTCCCTG  1656 CCTACCTGCAGCATCTCCTTTCC CTTTAAAATGACCATGTAGTGGC AAGCAGCCTTTTACTCTTCTGTT AGCTCTGG 409 RBX1 NM_014248.3   158- GATATTGTGGTTGATAACTGTGC   257 CATCTGCAGGAACCACATTATGG ATCTTTGCATAGAATGTCAAGCT AACCAGGCGTCCGCTACTTCAGA AGAGTGTA 410 RELA NM_021975.2   361- GATGGCTTCTATGAGGCTGAGCT   460 CTGCCCGGACCGCTGCATCCACA GTTTCCAGAACCTGGGAATCCAG TGTGTGAAGAAGCGGGACCTGG AGCAGGCTA 411 REPIN1 NM_014374.3  2491- TGTGTCCAGGCTCTTGTCTGAAC  2590 ACCGCAGCCCCTCCTTCGCTCCT TCCAGAGCTCAGCATGTCACGGC AAGGACTGCCGCATTGGTGATGG AGGGCCAG 412 REPS1 NM_00112861  1289- CACCAACCAGTACTCTTTTAACC 7.2  1388 ATGCATCCTGCTTCTGTCCAGGA CCAGACAACAGTACGAACTGTA GCATCAGCTACAACTGCCATTGA AATTCGTAG 413 RERE NM_00104268  5916- AACCCTCGACCCGAAACCCTCAC 2.1  6015 CAGATAAACTACAGTTTGTTTAG GAGGCCCTGACCTTCATGGTGTC TTTGAAGCCCAACCACTCGGTTT CCTTCGGA 414 RERE  NM_012102.3  7734- GCATTCTTGTTAGCTTTGCTTTT b  7833 CTCCCCATATCCCAAGGCGAAGC GCTGAGATTCTTCCATCTAAAAA ACCCTCGACCCGAAACCCTCACC AGATAAAC 415 RFWD2 NM_022457.6  2606- TTTTCTTTTCCCTCCTTTATGAC  2705 CTTTGGGACATTGGGAATACCCA GCCAACTCTCCACCATCAATGTA ACTCCATGGACATTGCTGCTCTT GGTGGTGT 416 RFX1 NM_002918.4  4187- ATAAAAATCACTATTTTGTGTGC  4286 TCCGCGTGCTATAGCTTTTGGGG CGGCCCTGCCCAGTCCCCGTGCC CACGGGGCTCCCTCTCCCGGTGG TGAAAGTG 417 RHOB NM_004040.3  1707- GGGAGGAGGGAGGATGCGCTGT  1806 GGGGTTGTTTTTGCCATAAGCGA ACTTTGTGCCTGTCCTAGAAGTG AAAATTGTTCAGTCCAAGAAACT GATGTTATT 418 RHOG NM_001665.3  1045- CTTTCCACACAGTTGTTGCTGCC  1144 TATTGTGGTGCCGCCTCAGGTTA GGGGCTCTCAGCCATCTCTAACC TCTGCCCTCGCTGCTCTTGGAAT TGCGCCCC 419 RHOU NM_021205.5  4174- TTGACAGACTCAAGAGAAACTA  4273 CCCAGGTATTACACAAGCCAAA ATGGGAGCAAGGCCTTCTCTCCA GACTATCGTAACCTGGTGCCTTA CCAAGTTGTG 420 RNASE2 NM_002934.2   331- TGACCTGTCCTAGTAACAAAACT   430 CGCAAAAATTGTCACCACAGTGG AAGCCAGGTGCCTTTAATCCACT GTAACCTCACAACTCCAAGTCCA CAGAATAT 421 RNF114 NM_018683.3  2246- AATTCAGATCATCTCAGAAGTCT  2345 GGAGGGAAATCTGGCGAAACCT TCGTTTGAGGGACTGATGTGAGT GTATGTCCACCTCACTGGTGGCA CCGAGAAAC 422 RNF19B NM_153341.3  2222- CCCCAGAGCCCAAGGTGCACCG  2321 AGCCCAAGTGCCCATATGAACCT CTCTGCCCTAGCCGAGGGACAAA CTGTCTTGAAGCCAGAAGGTGGA GAAGCCAGA 423 RNF214 NM_207343.3  2068- ACCTGTAAGCTATGTCTAATGTG  2167 CCAGAAACTCGTCCAGCCCAGTG AGCTGCATCCAATGGCGTGTACC CATGTATTGCACAAGGAGTGTAT CAAATTCT 424 RNF34 NM_025126.3  1619- CTTCTGTCCTCTTTGGATGAGAT  1718 CAGTGTCCACAAGTGGCCGACAT GGAACATGCTGAGCAGTGGCTCC TCTGAATGTTCACTTTATTAGTC ATGTATAT 425 C20orf NM_080748.2   274- CTCAGGATCGGAATGCGGGGTC 52   373 GAGAGCTGATGGGCGGCATTGG GAAAACCATGATGCAGAGTGGC GGCACCTTTGGCACATTCATGGC CATTGGGATGG 426 RPL26 NM_016093.2     4- CACTCAGGGTCTGAGGCAGCTAG L1   103 TAGCCGGAGGGTCACCATGAAG TTCAATCCCTTCGTTACCTCGGA CCGCAGTAAAAACCGCAAACGT CACTTCAATG 427 RPL3 NM_00103385  1072- AGAAGAAAGCATTCATGGGACC 3.1  1171 ACTGAAGAAAGACCGAATTGCA AAGGAAGAAGGAGCTTAATGCC AGGAACAGATTTTGCAGTTGGTG GGGTCTCAATA 428 RPL31 NM_000993.4    20- CTTGCAACTGCGGCTTTCCTTCT   119 CCCACAATCCTTCGCGCTCTTCC TTTCCAACTTGGACGCTGCAGAA TGGCTCCCGCAAAGAAGGGTGGC GAGAAGAA 429 RPL34 NM_000995.3   471- ACCTCACCTCAGCTTGAGAGAGC   570 CAGTTGTGTGCATCTCTTTCCAG TTTTGCATCCAGTGACGTCTGCT TGGCATCTTGAGATTGTTATGGT GAGAGTAT 430 RPL39L NM_052969.1   139- GCGGGTTCGGGTCGGTGACACGC   238 AGACCTGAGGGAGCTGGGCCCG CCTTTTCCGCCCGCGCCCCAGGC CCTTGCAGATCGAGATTTGCGTC CTAGAGTGG 431 KIAA NM_015203.4  4795- CCCCTTGGGTCCCTCACACAGAG 0460  4894 ACACCATCAGCCGGAGTGGTATA ATCTTACGGAGTCCCCGGCCAGA CTTTCGGCCTAGGGAACCTTTTC TCAGCAGA 432 RPS24 NM_001026.4   482- ATGAAGAAAGTCAGGGGGACTG   581 CAAAGGCCAATGTTGGTGCTGGC AAAAAGCCGAAGGAGTAAAGGT GCTGCAATGATGTTAGCTGTGGC CACTGTGGAT 433 RPS27L NM_015920.3   241- TAAAATGTCCAGGTTGCTACAAG   340 ATCACCACGGTTTTCAGCCATGC TCAGACAGTGGTTCTTTGTGTAG GTTGTTCAACAGTGTTGTGCCAG CCTACAGG 434 RPS6 NM_001010.2   172- GAATGGAAGGGTTATGTGGTCCG   271 AATCAGTGGTGGGAACGACAAA CAAGGTTTCCCCATGAAGCAGGG TGTCTTGACCCATGGCCGTGTCC GCCTGCTAC 435 RSL24 NM_016304.2  1232- TGGAGTGACACTACACTCTAGAA D1  1331 TTTCCACTTTGGAGAATACTCAG TTCCAACTTGTGATTCCTGATAG AACAGACTTTACTTTTCTAGCCC AGCATTGA 436 RWDD1 NM_00100746   998- TGGAGGATGATGAAGATGATCC 4.2  1097 AGACTATAATCCTGCTGACCCAG AGAGTGACTCAGCTGACTAATGG ACTGTCCCCATCTGCAGAGAGGC TTGACTGCC 437 RXRA NM_002957.5  5301- AGTAATTTTTAAAGCCTTGCTCT  5400 GTTGTGTCCTGTTGCCGGCTCTG GCCTTCCTGTGACTGACTGTGAA GTGGCTTCTCCGTACGATTGTCT CTGAAACA 438 S100A NM_005621.1   261- CAAGATGAACAGGTCGACTTTCA 12b   360 AGAATTCATATCCCTGGTAGCCA TTGCGCTGAAGGCTGCCCATTAC CACACCCACAAAGAGTAGGTAG CTCTCTGAA 439 S100A8 NM_002964.4   366- GTTAACTTCCAGGAGTTCCTCAT   465 TCTGGTGATAAAGATGGGCGTGG CAGCCCACAAAAAAAGCCATGA AGAAAGCCACAAAGAGTAGCTG AGTTACTGGG 440 SAMSN1 NM_022136.3  1024- ACCTGAGCCCCTATCCTTGAGCT  1123 CAGACATCTCCTTAAATAAGTCA CAGTTAGATGACTGCCCAAGGG ACTCTGGTTGCTATATCTCATCA GGAAATTCA 441 SAP NM_024545.3  3091- GATCTCCACCGAATAAACGAACT 130b  3190 GATACAGGGAAATATGCAGAGG TGTAAACTTGTGATGGATCAAAT CAGTGAAGCCAGAGACTCCATG CTTAAGGTTT 442 SAP130 NM_024545.3  3720- CGGTTCTTCTGCCTGACCTTCAA  3819 ATGCCCATGTTGGCCTTTTACAG CAGTGCCACGGCACCAAGCGAG CTGCCACATCTCACACTCTAAAG GGTTTGAAC 443 CIP29 NM_033082.3   622- AACTGGAACCACAGAGGATACA   721 GAGGCAAAGAAGAGGAAAAGAG CAGAGCGCTTTGGGATTGCCTGA TGAAAAGTTCCTGATACTTTCTG TTCTCCAGTG 444 SFRS2 NM_004719.2  4203- AGTTCTTCTCATGTAAGTAATAA IP  4302 CATGAGTACACCAGTTTTGCCTG CTCCGACAGCAGCCCCAGGAAA TACGGGAATGGTTCAGGGACCA AGTTCTGGTA 445 SFRS15 NM_020706.2  3635- GAGAGAAGGAAGAAGCCCGAGG  3734 AAAGGAAAAGCCTGAGGTGACA GACAGGGCAGGTGGTAACAAAA CCGTTGAACCTCCCATTAGCCAA GTGGGAAATGT 446 RBM16 NM_014892.4  4111- TGATTATTTTGAAGGGGCCACTT  4210 CTCAACGAAAAGGTGATAATGT GCCTCAGGTTAATGGTGAAAATA CAGAGAGACATGCTCAGCCACC ACCTATACCA 447 SDHA NM_004168.3  2042- GTCACTCTGGAATATAGACCCGT  2141 GATCGACAAAACTTTGAACGAG GCTGACTGTGCCACCGTCCCGCC AGCCATTCGCTCCTACTGATGAG ACAAGATGT 448 SEC24C NM_198597.2  4194- AGGCAGAGGCAGCTGGAGCGCC  4293 GTTCTCTCCTGCTGGGACACCGC TTGGGCTTTGGTATTGACTGAGT GGCTGACAGTTATCTTCCAACCC CAACTGGCT 449 SEMG1 NM_003007.2  1291- GGCAGACACCAACATGGATCTC  1390 ATGGGGGATTGGATATTGTAATT ATAGAGCAGGAAGATGACAGTG ATCGTCATTTGGCACAACATCTT AACAACGACC 450 SERPIN NM_005024.1   891- AGACAGTTATGATCTCAAGTCAA B10   990 CCCTGAGCAGTATGGGGATGAGT GATGCCTTCAGCCAAAGCAAAG CTGATTTCTCAGGAATGTCTTCA GCAAGAAAC 451 SETD2 NM_014159.6  7956- TGGTTAGAAGCCATCAGAGGTGC  8055 AAGGGCTTAGAAAAGACCCTGG CCAGACCTGACTCCACTCTTAAA CCTGGGTCTTCTCCTTGGCGGTG CTGTCAGCG 452 SFMBT1 NM_00100515  2844- AAGGATCGAAGTTGCTGAAAGG 8.2  2943 CTTCACCTGGACAGTAACCCCTT GAAGTGGAGTGTGGCAGACGTT GTGCGGTTCATCAGATCCACTGA CTGTGCTCCA 453 SFPQ NM_005066.2  2800- GGTTATGTAAGCAAAGCTGAACT  2899 GTAAATCTTCAGGAATATGTATT AAGATTGTGGAATGGGTGTAAG ACAATTGGTAGGGGGTGAAAGT GGGTTTGATT 454 SGK1 NM_005627.3  1622- ACGAGCGTTAGAGTGCCGCCTTA  1721 GACGGAGGCAGGAGTTTCGTTA GAAAGCGGACGCTGTTCTAAAA AAGGTCTCCTGCAGATCTGTCTG GGCTGTGATG 455 SGK NM_005627.3   173- GAAGCAGAGGAGGATGGGTCTG   272 AACGACTTTATTCAGAAGATTGC CAATAACTCCTATGCATGCAAAC ACCCTGAAGTTCAGTCCATCTTG AAGATCTCC 456 SGK1b NM_005627.3  1814- GGATATGCTGTGTGAACCGTCGT  1913 GTGAGTGTGGTATGCCTGATCAC AGATGGATTTTGTTATAAGCATC AATGTGACACTTGCAGGACACTA CAACGTGG 457 SH2D3C NM_170600.2  2795- AGCACCCCAAGGACACTGTGATC  2894 AACCCGAGAATGTTCTGGGTTCA ACTCAAGCATCTCCCTTGCACCT CCAGGGTCCTGCGTGGACTCTGG GTTCCATC 458 SIK1 NM_173354.3  4185- TCGCTCATAAAGAAGTTTTTGGG  4284 ATGGGAGAGAATCCAGACCATC TTGGGGCAGCCAGGCCCTTGCCT TCATTTTTACAGAGGTAGCACAA CTGATTCCA 459 SIN3A NM_015477.2  4666- TTTATTCCTGACGATTCCCTTGC  4765 TGCCTACCCTTTTCTCTCCTCTG GTTCTCAACCTCAACGAGTTCAA ATCAGTTGTCCTTTTTAGCTCCC GTGGAACT 460 SLAMF8 NM_020125.2  3173- AACAAATATTGATTGAGGGCGCT  3272 GCATGTGCTGGGTACATTTCTTG GCACTTGGGAATCAGTAGTCAAG CGAAACCCTTGCCTTTGAGAGTT TATGGTCT 461 SLC11 NM_000578.3  2072- GCAGGATAGAGTGGGACAGTTC A1  2171 CTGAGACCAGCCAACCTGGGGG CTTTAGGGACCTGCTGTTTCCTA GCGCAGCCATGTGATTACCCTCT GGGTCTCAGT 462 SLC15 NM_021082.3  2548- AACTCATTAAAACTTGTGCAGTG A2  2647 TTGCTGGAGCTGGCCTGGTGTCT CCAAATGACCATGAAAATACAC ACGTATAATGGAGATCATTCTCT GTGGGTATG 463 SLC25 NM_000387.5  1511- ATCTTCTTCAGTCCCTAGCCAGG A20  1610 AATACCCATTTGATTTCCAGGGT GCCATCTAATCCTGGGCTGTACA TGTGGATATGGACTTGAGGCCCA CCTCTGTG 464 SLC25 NM_016612.2  1217- TCCAGCCCCTTGCCCTCTCCTCA A37  1316 CACGTAGATCATTTTTTTTTTGC AGGGTGCTGCCTATGGGCCCTCT GCTCCCCAATGCCTTAGAGAGAG GAGGGGAC 465 SLC45 NM_033102.2  2455- AGTTTCTAGGATGAAACACTCCT A3  2554 CCATGGGATTTGAACATATGAAA GTTATTTGTAGGGGAAGAGTCCT GAGGGGCAACACACAAGAACCA GGTCCCCTC 466 SLC6 NM_003044.4  3220- GATATTGCTAACTGATCACAGAT A12  3319 TCTTTCCCACCTCACAATCCTTC CGAATGTGCTCCAGGCAGCACCA TTTGCCATCCTGCTTCTAACGCA AACCCCTG 467 SLC6 NM_003043.5  4438- ATTCTAGACCAAAGACACAGGC A6  4537 AGACCAAGTCCCCAGGCCCCGCC TGGAAGGAAGTCGTTCCTCAACT CTCCCCAAGGCACCTGTCTCCAA TCAGAGCCC 468 SLC9 NM_004252.3  1811- ATTAACATGATTTTCCTGGTTGT A3R1  1910 TACATCCAGGGCATGGCAGTGGC CTCAGCCTTAAACTTTTGTTCCT ACTCCCACCCTCAGCGAACTGGG CAGCACGG 469 C14orf NM_031210.5    46- CGGCCTCAGCAGCGAGAGGTGC 156   145 TGCGGCGCTGCGTAGAAGTATCA ATCAGCCGGTTGCTTTTGTGAGA AGAATTCCTTGGACTGCGGCGTC GAGTCAGCT 470 SMAR NM_003074.3  5281- CAATGGCCAGGGTTTTACCTACT CC1  5380 TCCTGCCAGTCTTTCCCAAAGGA AACTCATTCCAAATACTTCTTTT TTCCCCTGGAGTCCGAGAAGGAA AATGGAAT 471 SNORA NR_002984.1    30- CTCGTGGGACTCTAGAGGGAGTC 56   129 AGTCTGCAACAGTAAGTGGTGA GTTCTTCTGTCCAGCGTCAGTAT TTTGATGGTGGCTTTAGACTTGC CAGATAACA 472 SNX11 NM_152244.1  2261- CCCTCCCTGTCGCCCACTCCTCC  2360 CTCCTCTGGCTATCCTACCCTGT CTGTGGGCTCTTTTACTACCAGC CTATGCTGTGGGACTGTCATGGC ATTTAGTT 473 SOCS1 NM_003745.1  1026- TTAACTGTATCTGGAGCCAGGAC  1125 CTGAACTCGCACCTCCTACCTCT TCATGTTTACATATACCCAGTAT CTTTGCACAAACCAGGGGTTGGG GGAGGGTC 474 SP2 NM_003110.5  2701- GGGGGCAATGATGAGCATATGAA  2800 TTTTTTCTCACTCTAGCAATTCC CTTTTCTAAATGACACAGCATTT AAACTCAAATCTGGATTCAGATA ACAGCACC 475 SPA17 NM_017425.3   176- CAAGGATTTGGGAATCTTCTTGA   275 AGGGCTGACACGCGAGATTCTG AGAGAGCAACCGGACAATATAC CAGCTTTTGCAGCAGCCTATTTT GAGAGCCTTC 476 SPEN NM_015001.2 11995- GTATTGCCCACTCATTTGTATAA 12094 GTGCGCTTCGGTACAGCACGGGT CCTGCTCCCGCGATGTGGAAGTG TCACACGGCACCTGTACAAAAA GACTGGCTA 477 SPINK5 NM_006846.3  2596- GAGCAATGACAAAGAGGATCTG  2695 TGTCGTGAATTTCGAAGCATGCA GAGAAATGGAAAGCTTATCTGC ACCAGAGAAAATAACCCTGTTCG AGGCCCATAT 478 SPN NM_003123.3  2346- AGTGCCTGCGTGTGTCCACTCGT  2445 GGGTGTGGTTTGTGTGCAAGAGC TGAGGATTTGGCGATGCTTGGGA GGGGTAGTTGTGGGTACAGACG GTGTGGGGG 479 SREB NM_00100529  3985- CCCCTCCTTGCTCTGCAGGCACC F1 1.2  4084 TTAGTGGCTTTTTTCCTCCTGTG TACAGGGAAGAGAGGGGTACATT TCCCTGTGCTGACGGAAGCCAAC TTGGCTTT 480 SFRS4 NM_005626.4  2080- TACTCATGGCCCACAGTAGAATA  2179 TCCAAAACGCCTTGGCTTTCAGG CCTGGCCTTTCCTACAGGGAGCT CAGTAACCTGGACGGCTCTAAGG CTGGAATG 481 ST6GA NM_003032.2  3783- CTGATTTTAATCTTCGAATCATG L1  3882 ACACTGAGTGCAGAGGAGGTGG CATTCCGACAGCAGGACATACAT GTTGGTGTGAAGACTGGGACGA CACTGGGTAG 482 STAG3 NM_012447.3  3424- AAGTGCCTGCAGCATGTCTCCCA  3523 GGCACCTGGCCATCCCTGGGGCC CAGTCACCACCTACTGCCACTCC CTCAGCCCTGTGGAGAACACAGC AGAGACCA 483 STAMBP NM_006463.4  1926- TTTCCTGTGGTTTATGGCAATAT  2025 GAATGGAGCTTATTACTGGGGTG AGGGACAGCTTACTCCATTTGAC CAGATTGTTTGGCTAACACATCC CGAAGAAT 484 STAT6 NM_003153.4  3725- ACTGTGCCCAAGTGGGTCCAAGT  3824 GGCTGTGACATCTACGTATGGCT CCACACCTCCAATGCTGCCTGGG AGCCAGGGTGAGAGTCTGGGTC CAGGCCTGG 485 STIP1 NM_006819.2  1906- CCCGGGGAAGACACAGAGACTC  2005 GTACCTGCGCTGTTTGTGCCGCC GCTGCCTCTGGGCCCTCCCAGCA CACGCATGGTCTCTTCACCGCTG CCCTCGAGT 486 STK16 NM_003691.2  1420- GGGGTAGCGGGGTCAGGACAATC  1519 ATCTCAGTCCTGCATCTTTTCTT CTGCTTTCTTCCCTCCAAGAGCA AAACCTGGGCAAGGGGACTTAC TGAGTGGGG 487 STK38 NM_007271.3  3269- TTGTCAGTGAAACTACTTTGGAT  3368 TTTAACCTCTTAGAGGAAGAAAA AAGGTTAGGGAAGTGTCAACTCT GGATGAAGGTGATGTGTTTGCCT CTCAGTCT 488 STOM NM_004099.5  2953- TTCTGCCTTGTGAATTCGTAGTC  3052 CAATCAGCTGAAATTAAATCACT TGGGAGGGACGCATAGAAGGAG CTCTAGGAACACAGTGCCAGTGC AGAAGTTTC 489 SYNJ1 NM_003895.3  4746- CCCTCTGCTCCCGCCCGGCACCA  4845 GCCCTCCAGTAGATCCTTTCACG ACCTTGGCCTCTAAGGCTTCACC CACACTGGACTTTACAGAAAGAT AACGCCAT 490 TAPBP NM_003190.4  3397- CTTGCCCTCCCTGGGTCGCAGAC  3496 GAGGTCGGCCTCGTCATTCCCCG CAGACCGCCGCGCGTCCCTCTTG TGCGGTTCACCACAGTTGTATTT AAGTGATC 491 TAX1 NM_00107986  2081- CAGCCAGCCTGCTCGAAACTTTA BP1 4.2  2180 GTCGGCCTGATGGCTTAGAGGAC TCTGAGGATAGCAAAGAAGATG AGAATGTGCCTACTGCTCCTGAT CCTCCAAGT 492 TBC1 NM_015188.1  5451- TTCCAAGGAATGCACTAAGCCTT D12  5550 CAGTCTTTTTAGACTGACAGTAC TGGCAGCTAAAATATTGTACTGT ATCTTCTCTTGAGCCCAGTATGT AGGAAATA 493 TBCE NM_00107951  1541- TATGCTGAAAAACCAGCTACTAA 5.2  1640 CACTGAAGATAAAATACCCTCAT CAACTTGATCAGAAAGTCCTGGA GAAACAACTGCCGGGCTCCATG ACAATTCAA 494 TBK1 NM_013254.2  1611- ACCAGTCTTCAGGATATCGACAG  1710 CAGATTATCTCCAGGTGGATCAC TGGCAGACGCATGGGCACATCA AGAAGGCACTCATCCGAAAGAC AGAAATGTAG 495 TBP NM_003194.4  1441- TGTAAGTGCCCACCGCGGGATGC  1540 CGGGAAGGGGCATTATTTGTGCA CTGAGAACACCGCGCAGCGTGA CTGTGAGTTGCTCATACCGTGCT GCTATCTGG 496 TCF20 NM_181492.2  6765- CCAGGCCTGTGTTGCCAGAGCTG  6864 GCAGTGTGAGCTGTAGGCAGGG ACGGGGAGGGACTGTCGCTGTG ATCAGAGTGGGTTAAGCTGACCA GGAACACCCA 497 TCF7L2 NM_030756.4  2067- GGCCCACCTGTCCATGATGCCTC  2166 CGCCACCCGCCCTCCTGCTCGCT GAGGCCACCCACAAGGCCTCCG CCCTCTGTCCCAACGGGGCCCTG GACCTGCCC 498 TCP1 NM_030752.2   254- GTGTTCGGTGACCGCAGCACTGG   353 GGAAACGATCCGCTCCCAAAAC GTTATGGCTGCAGCTTCGATTGC CAATATTGTAAAAAGTTCTCTTG GTCCAGTTG 499 TFCP2 NM_005653.4  2271- CCTCTGAAAACGGCCCTCTTGAA  2370 GGGGGATATGAATGGAGATTTG AAGGTCTGCAAGAACCTGACTCG TCTGACTGTGTGTGGAGGAGTCC AGGCCATGG 500 TGIF1 NM_003244.2  1041- ACCTCAACCAGGACTTCAGTGGA  1140 TTTCAGCTTCTAGTGGATGTTGC ACTCAAACGGGCTGCAGAGATG GAGCTTCAGGCAAAACTTACAGC TTAACCCAT 501 TGIF1b NM_173208.1   691- CCCCGGGATCAGTTTTGGCTCGT   790 CCATCAGTGATCTGCCATACCAC TGTGACTGCATTGAAAGATGTCC CTTTCTCTCTCTGCCAGTCGGTC GGTGTGGG 502 TIAM1 NM_003253.2  5293- CCTAACTCTGCCCACCCTCCTGT  5392 ACCGTCGACAAGAATGTCCCCTT AGGTCGCGCTCTTGCACACACGG TTTTGGCAGCTGACTTGGTTCTG AAGCCATG 503 TIMM8B ENST0000050   339- GAATGACAGAAGCAAAGGACTT 4148.1   438 GTTACTAAGCAGATTTAAGGGTC AGTGGGGGAAGGCTATCAACCC ATTGTCAGATCAGCATCAGGCTG TTATCAAGTC 504 TM2D2 NM_078473.2  2970- ACCCATCATCCATCTGCCCACAA  3069 ACCTGGCCAAATGTGATACAACC TGAAAACCTGATGGACTAAAGG AGTACTATTTAACAATTGATTGC CTTTGCACT 505 TM9SF1 NM_006405.6  1996- CGCTGGTGGTGGCGATCTGTGCT  2095 GAGTGTTGGCTCCACCGGCCTCT TCATCTTCCTCTACTCAGTTTTC TATTATGCCCGGCGCTCCAACAT GTCTGGGG 506 CCDC72 NM_015933.4   124- GAGGAGCAGAAGAAACTCGAGG   223 AGCTAAAAGCGAAGGCCGCGGG GAAGGGGCCCTTGGCCACAGGT GGAATTAAGAAATCTGGCAAAA AGTAAGCTGTTC 507 TMBIM6 NM_003217.2  2282- CTCTCCCTATTCACAACCAGTGC  2381 ACAGTTTGACACAGTGGCCTCAG GTTCACAGTGCACCATGTCACTG TGCTATCCTACGAAATCATTTGT TTCTAAGT 508 TMC8 NM_152468.4  2238- AGGCCAATGCCAGGGCCATCCA  2337 CAGGCTCCGGAAGCAGCTGGTGT GGCAGGTTCAGGAGAAGTGGCA CCTGGTGGAGGACCTGTCGCGAC TGCTGCCGGA 509 TMC01 NM_019026.3   992- TCATTTACATAAGTATTTTCTGT  1091 GGGACCGACTCTCAAGGCACTGT GTATGCCCTGCAAGTTGGCTGTC TATGAGCATTTAGAGATTTAGAA GAAAAATT 510 TMEM NM_00110082  7652- AGGAGAATAAATGTTGGAGGGG 170B 9.2  7751 TAATACACAAAAACAAAGGCAT ATTTGATGAAGTACCCTGTGTTA TGTGAACACAATTTCCCCTTCTG TTAAGACTAT 511 TMEM NM_00108054  1313- GCTCTGTGAAGGCAATGAGTGTC 218 6.2  1412 ACTTCCCTCTGCTCTAATAAAGC AATAAATAATAGCTAAAGGGCT GACTTTCACTTCGAACTCTTGGC CACGGCTTT 512 TMEM70 NM_017866.5  1952- GGTGGTTAGCTATACGGGAAATG  2051 GTAAGTAGTGTTGTCTTCAGTAT CTTAATTTGTTTCTGCAACTGTG CACTCCTCCCTTGGTGGCACCCT ATGGGTGT 513 TMSB4X NM_021109.3   286- TTAACTTTGTAAGATGCAAAGAG   385 GTTGGATCAAGTTTAAATGACTG TGCTGCCCCTTTCACATCAAAGA ACTACTGACAACGAAGGCCGCG CCTGCCTTT 514 TNFR NM_001561.5  1848- GCCTGGAGGAAGTTTTGGAAAG SF9  1947 AGTTCAAGTGTCTGTATATCCTA TGGTCTTCTCCATCCTCACACCT TCTGCCTTTGTCCTGCTCCCTTT TAAGCCAGG 515 TNF NM_003808.3   811- AGTCAGAGAGCCGGCACTCTCA SF13   910 GTTGCCCTCTGGTTGAGTTGGGG GGCAGCTCTGGGGGCCGTGGCTT GTGCCATGGCTCTGCTGACCCAA CAAACAGAG 516 TNFSF8 NM_001244.3   519- CCCTCAAAGGAGGAAATTGCTCA   618 GAAGACCTCTTATGTATCCTGAA AAGGGCTCCATTCAAGAAGTCAT GGGCCTACCTCCAAGTGGCAAA GCATCTAAA 517 TOMM7 NM_019059.2   251- TCTGGCTCGGATAAGAGATGGG   350 ACATCATTCAGTCACTAGTTGGA TGGCACAAGGCTCTTCACAGACG CATCTGTAGCAGAGTGGATCTTG TACTAACTT 518 TP53 NM_005657.2  5591- TACTTCCTGTGCCTTGCCAGTGG BP1  5690 GATTCCTTGTGTGTCTCATGTCT GGGTCCATGATAGTTGCCATGCC AACCAGCTCCAGAACTACCGTAA TTATCTGT 519 TPR NM_003292.2  7194- TCTCCCCTCCACCAGCCAGGATC  7293 CTCCTTCTAGCTCATCTGTAGAT ACTAGTAGTAGTCAACCAAAGCC TTTCAGACGAGTAAGACTTCAGA CAACATTG 520 TPT1 NM_003295.3    18- GCCTGCGTCGCTTCCGGAGGCGC   117 AGCGGGCGATGACGTAGAGGGA CGTGCCCTCTATATGAGGTTGGG GAGCGGCTGAGTCGGCCTTTTCC GCCCGCTCC 521 TRAF NM_147686.3  2449- GCCAGTGTCCCATATGTTCCTCC 3IP2  2548 TGACAGTTTGATGTGTCCATTCT GGGCCTCTCAGTGCTTAGCAAGT AGATAATGTAAGGGATGTGGCA GCAAATGGA 522 TRAF6 NM_145803.1  1840- CACCCGCTTTGACATGGGTAGCC  1939 TTCGGAGGGAGGGTTTTCAGCCA CGAAGTACTGATGCAGGGGTAT AGCTTGCCCTCACTTGCTCAAAA ACAACTACC 523 LBA1 NM_014831.2 10132- CTGGGAAACCTTCATGCCTCTCT 10231 GATGGTTACTGCCCACCCTTACC CCACCCCTCAGCTCAGCCTGGTA TGGAAAGCAAGGTGCACGTTGG TCTTTGATT 524 TRIM21 NM_003141.3  1637- TCTGCAGAGGCATCCGGATCCCA  1736 GCAAGCGAGCTTTAGCAGGGAA GTCACTTCACCATCAACATTCCT GCCCCAGATGGCTTTGTGATTCC CTCCAGTGA 525 TRIM32 NM_012210.3  2681- GTGCTACCAAAGGGGATACACA  2780 AGCCCTTTAGGAAGCAGTACCTC TCGCCTGGAGGATCTGTGCCATC TTGGATTGAGAATTGCAGATGTG ACAGAATGG 526 TRIM39 NM_021253.3  3141- CTGCTATTCGGGTAATCTTCACA  3240 GAAATGACTGAGAGAAGAATCT GCAGTTTACTGAGGGCATTTCAG TTCCTCCTACCACCTCAACAGGA CTTTGTCCA 527 TRIM NM_172016.2  2841- CTCTATACCAATAAGTCAGTCAC 39b  2940 CTTGCTCCTCTCCAGAGGCAAAG TGGAAGAGATCCTGCAAGACAC ATCTATCCTTTCACAGTGTTCCC AAGGGAACT 528 TRRAP NM_003496.3 12169- AGTTGATGAACCCATCATGCTGG 12268 TTTTTCTCTGAGCACAAAGTTTT AGGCTGTACACAGCCAGCCTTGG GAATCTCGTTGAGCGTTCGGCGT GGATCCAC 529 TSC1 NM_000368.4  8068- CCCCAGACCAACCCTTCCCTCCC  8167 TTTCCCCACCTCTTACAGTGTTT GGGACAGGAGGGTATGGTGCTGC TCTGTGTAGCAAGTACTTTGGCT ATTGAAAGA 530 TTC9 NM_015351.1  4050- TACTAATCAGGCATCTGACCTGC  4149 ACTGTCATCCCCTGCCTGGACTT TTGCGATGGACTCTTTGGGGGAA AAACTAACGCTTTTTAATTATTG TGAAAGCA 531 TTN NM_133378.4   850- TCGACTGCTCAGATCTCAGAATC   949 AAGACAAACCCGAATTGAAAAG AAGATTGAAGCCCACTTTGATGC CAGATCAATTGCAACAGTTGAGA TGGTCATAG 532 TUBB NM_178014.2  2223- CAAAAAAGAATGAACACCCCTG  2322 ACTCTGGAGTGGTGTATACTGCC ACATCAGTGTTTGAGTCAGTCCC CAGAGGAGAGGGGAACCCTCCT CCATCTTTTT 533 TUG1 NR_002323.2  7082- TAAGCTAGAGGTCATGGTCACTG  7181 AAATTACTTTCCAAAGTGGAAGA CAAAATGAAACAGGAACTGAGG GAATATTTAAGATCCCACAGAAG CGTAAAAAT 534 TXN NM_003329.3   152- TTGGATCCATTTCCATCGGTCCT   251 TACAGCCGCTCGTCAGACTCCAG CAGCCAAGATGGTGAAGCAGATC GAGAGCAAGACTGCTTTTCAGGA AGCCTTGG 535 TXNDC NM_032731.3   378- TCATCTACTGCCAAGTAGGAGAA 17   477 AAGCCTTATTGGAAAGATCCAAA TAATGACTTCAGAAAAAACTTGA AAGTAACAGCAGTGCCTACACTA CTTAAGTA 536 TXNRD1 NM_00109377  3348- CTCAGTTGCAGCACTGAGTGGTC 1.2  3447 AAAATACATTTCTGGGCCACCTC AGGGAACCCATGCATCTGCCTGG CATTTAGGCAGCAGAGCCCCTGA CCGTCCCC 537 TXNR NM_182743.2  2438- TGTTGCATGGAAGGGATAGTTTG D1b  2537 GCTCCCTTGGAGGCTATGTAGGC TTGTCCCGGGAAAGAGAACTGTC CTGCAGCTGAAATGGACTGTTCT TTACTGAC 538 U2AF2 NM_007279.2  2871- TTTATGGCCAAACTATTTTGAAT  2970 TTTGTTGTCCGGCCCTCAGTGCC CTGCCCTCTCCCTTACCAGGACC ACAGCTCTGTTCCTTCGGCCTCT GGTCCTCT 539 UBA1 NM_003334.3  3307- CCGCCACGTGCGGGCGCTGGTGC  3406 TTGAGCTGTGCTGTAACGACGAG AGCGGCGAGGATGTCGAGGTTC CCTATGTCCGATACACCATCCGC TGACCCCGT 540 UBC NM_021009.3  1876- TGCAGATCTTCGTGAAGACCCTG  1975 ACTGGTAAGACCATCACTCTCGA AGTGGAGCCGAGTGACACCATT GAGAATGTCAAGGCAAAGATCC AAGACAAGGA 541 UBE2G1 NM_003342.4   685- ACGCTGGCTCCCTATCCACACTG   784 TGGAAACCATCATGATTAGTGTC ATTTCTATGCTGGCAGACCCTAA TGGAGACTCACCTGCTAATGTTG ATGCTGCG 542 UBE2I NM_194259.2   288- CTGCTCTGCTGACTGGGGAAGTC   387 ATCGTGCCACCCAGAACCTGAGT GCGGGCCTCTCAGAGCTCCTTCG TCCGTGGGTCTGCCGGGGACTGG GCCTTGTC 543 UBTF NM_00107668  2724- GGGGGTCCCAAAGAGTTTGATG 3.1  2823 AGGCCCTCCACACCTGCGGCCCA ATCCAAGGTGGGGTGGAAGCTT GGGGAAGACCCATTCCTTCCCAG AGGGGCCTGC 544 UQCRQ NM_014402.4    97- TGACGCGGATGCGGCATGTGATC   196 AGCTACAGCTTGTCACCGTTCGA GCAGCGCGCCTATCCGCACGTCT TCACTAAAGGAATCCCCAATGTT CTGCGCCG 545 USP16 NM_00103241  2487- TCTATTCCTTATATGGAGTTGTT 0.1  2586 GAACACAGTGGTACTATGAGGTC GGGGCATTACACTGCCTATGCCA AGGCAAGAACCGCAAATAGTCAT CTCTCTAA 546 USP21 NM_012475.4  1499- CCTTTTCACTAAGGAAGAAGAGC  1598 TAGAGTCGGAGAATGCCCCAGT GTGTGACCGATGTCGGCAGAAA ACTCGAAGTACCAAAAAGTTGA CAGTACAAAGA 547 USP34 NM_014709.3 10104- AGGAGCACACTGTAGACAGCTG 10203 CATCAGTGACATGAAAACAGAA ACCAGGGAGGTCCTGACCCCAA CGAGCACTTCTGACAATGAGACC AGAGACTCCTC 548 USP5 NM_003481.2  2720- AGAGCAGAGGGGCAGCGATAGA  2819 CTCTGGGGATGGAGCAGGACGG GGACGGGAGGGGCCGGCCACCT GTCTGTAAGGAGACTTTGTTGCT TCCCCTGCCCC 549 USP9Y NM_004654.3    86- GGTGTGGAAAGACTTTTCTGGGC   185 TCAGAGGTGAAACTGACCCTTGT GTATCAGCAGCATTTCTGACTGA CTGAGAGAGTGTAGTGATTAACA GAGTTGTG 550 VPS37C NM_017966.4  2579- TTATAAAGAGAAATCACTAATGG  2678 ACTCTACTGGTTTGAGTGCTTCT GAGCTGGATGACCGACCGCCTGT ATGTTTGTGTAATTAATTGCCAT AATAAACT 551 WDR1 NM_005112.4  2325- AACTGTTGCCTGTCAGTGTTTAC  2424 AAACTAGTGCGTTGACGGCACCG TGTCCAAGTTTTTAGAACCCTTG TTAGCCAGACCGAGGTGTCCTGG TCACCGTT 552 WDR91 NM_014149.3  2777- CAGGCTCTCCTGTTGCTTTGCCA  2876 TGGAGCCAGGTCAGCTCTCTGTC TGTTCTGCTGGGTAACAAGGTTT GGCAGTTCCTGTTTCTCTGGGCT TAAGTCAA 553 XCL2 NM_003175.3   378- GTAGTCTCTGGCACCCTGTCCGT   477 CTCCAGCCAGCCAGCTCATTTCA CTTTACACCCTCATGGACTGAGA TTATACTCACCTTTTATGAAAGC ACTGCATG 554 XPC NR_027299.1  3168- CTGGATGGTGGTGCATCCGTGAA  3267 TGCGCTGATCGTTTCTTCCAGTT AGAGTCTTCATCTGTCCGACAAG TTCACTCGCCTCGGTTGCGGACC TAGGACCA 555 YPEL1 NM_013313.4  3672- GCTCATTTTTAAACCAAATGAAC  3771 AGACCATGAGCTGGCTTCAGGG GAAGTGCTATTCACAGGACCATA TCCACCACCCTCTTAAATTCCTA AACAATATC 556 ZMIZ1 NM_020338.3  7171- ATGATCACAGGTGATTCACACGT  7270 ACACACATAAACACACCCACCA GTGCAGCCTGAAGTAACTCCCAC AGAAACCATCATCGTCTTTGTAC ATCGTATGT 557 ZNF143 NM_003442.5  2292- TATCAGATCACAAACTCCTAGAG  2391 TCTACATGCAAGACTAGTAAAGT CTTATGGAGTCTTATGATGGATT TTTAACTTCCCGTGGAAAAAAAA ATAAAGGC 558 ZNF239 NM_00109928  1496- AGAGCTCCAACCTTCACATCCAC 3.1  1595 CAGCGGGTTCACAAGAAAGATC CTCGCTAACTGACATTAGCCCAT TCAGGTCTTCACAGCGCTCATAC TGTAAAAAC 559 ZNF341 NM_032819.4  3247- CAGACGGTTCCCCACAGCATCCT  3346 CAGACAGCTCTGTGATGTAGCTT TTAGGAGGCACTCAGGTGTCACG GCTAGACTGCAGCTATGAGACA GATCTGGCT

C. Polymerase Chain Reaction (PCR) Techniques

Another suitable quantitative method is RT-PCR, which can be used to compare mRNA levels in different sample populations, in normal and tumor tissues, to characterize patterns of gene expression, to discriminate between closely related mRNAs, and to analyze RNA structure. The first step is the isolation of mRNA from a target sample (e.g., typically total RNA isolated from human PBMC). mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples.

General methods for mRNA extraction are well known in the art, such standard textbooks of molecular biology. In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, according to the manufacturer's instructions. Exemplary commercial products include TRI-REAGENT, Qiagen RNeasy mini-columns, MASTERPURE Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), Paraffin Block RNA Isolation Kit (Ambion, Inc.) and RNA Stat-60 (Tel-Test). Conventional techniques such as cesium chloride density gradient centrifugation may also be employed.

The first step in gene expression profiling by RT-PCR is the reverse transcription of the RNA template into cDNA, followed by its exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avilo myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of expression profiling. See, e.g., manufacturer's instructions accompanying the product GENEAMP RNA PCR kit (Perkin Elmer, Calif, USA). The derived cDNA can then be used as a template in the subsequent RT-PCR reaction.

The PCR step generally uses a thermostable DNA-dependent DNA polymerase, such as the Taq DNA polymerase, which has a 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonuclease activity. Thus, TAQMAN® PCR typically utilizes the 5′-nuclease activity of Taq or Tth polymerase to hydrolyze a hybridization probe bound to its target amplicon, but any enzyme with equivalent 5′ nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction. In one embodiment, the target sequence is shown in Table III. A third oligonucleotide, or probe, is designed to detect nucleotide sequence located between the two PCR primers. The probe is non-extendible by Taq DNA polymerase enzyme, and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are located close together as they are on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the released reporter dye is free from the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment. In a preferred embodiment, the 5′ nuclease procedure is run on a real-time quantitative PCR device such as the ABI PRISM 7900® Sequence Detection System®. The system amplifies samples in a 96-well format on a thermocycler. During amplification, laser-induced fluorescent signal is collected in real-time through fiber optic cables for all 96 wells, and detected at the CCD. The system includes software for running the instrument and for analyzing the data. 5′-Nuclease assay data are initially expressed as Ct, or the threshold cycle. As discussed above, fluorescence values are recorded during every cycle and represent the amount of product amplified to that point in the amplification reaction. The point when the fluorescent signal is first recorded as statistically significant is the threshold cycle (Ct).

To minimize errors and the effect of sample-to-sample variation, RT-PCR is usually performed using an internal standard. The ideal internal standard is expressed at a constant level among different tissues, and is unaffected by the experimental treatment. RNAs most frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin.

Real time PCR is comparable both with quantitative competitive PCR, where internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a normalization gene contained within the sample, or a housekeeping gene for RT-PCR.

In another PCR method, i.e., the MassARRAY-based gene expression profiling method (Sequenom, Inc., San Diego, CA), following the isolation of RNA and reverse transcription, the obtained cDNA is spiked with a synthetic DNA molecule (competitor), which matches the targeted cDNA region in all positions, except a single base, and serves as an internal standard. The cDNA/competitor mixture is PCR amplified and is subjected to a post-PCR shrimp alkaline phosphatase (SAP) enzyme treatment, which results in the dephosphorylation of the remaining nucleotides. After inactivation of the alkaline phosphatase, the PCR products from the competitor and cDNA are subjected to primer extension, which generates distinct mass signals for the competitor- and cDNA-derived PCR products. After purification, these products are dispensed on a chip array, which is pre-loaded with components needed for analysis with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. The cDNA present in the reaction is then quantified by analyzing the ratios of the peak areas in the mass spectrum generated.

Still other embodiments of PCR-based techniques which are known to the art and may be used for gene expression profiling include, e.g., differential display, amplified fragment length polymorphism (iAFLP), and BeadArray™ technology (Illumina, San Diego, CA) using the commercially available Luminex100 LabMAP system and multiple color-coded microspheres (Luminex Corp., Austin, Tex.) in a rapid assay for gene expression; and high coverage expression profiling (HiCEP) analysis.

D. Microarrays

Differential gene expression can also be identified, or confirmed using the microarray technique. Thus, the expression profile of lung cancer-associated genes can be measured in either fresh or paraffin-embedded tissue, using microarray technology. In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. Just as in the other methods and compositions herein, the source of mRNA is total RNA isolated from whole blood of controls and patient subjects.

In one embodiment of the microarray technique, PCR amplified inserts of cDNA clones are applied to a substrate in a dense array. In one embodiment, all 559 nucleotide sequences from Table III are applied to the substrate. The microarrayed genes, immobilized on the microchip, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pairwise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels. Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols.

Other useful methods summarized by U.S. Pat. No. 7,081,340, and incorporated by reference herein include Serial Analysis of Gene Expression (SAGE) and Massively Parallel Signature Sequencing (MPSS). Briefly, serial analysis of gene expression (SAGE) is a method that allows the simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript. First, a short sequence tag (about 10 to 14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules, that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag. For more details see, e.g. Velculescu et al., Science 270:484 487 (1995); and Velculescu et al., Cell 88:243 51 (1997), both of which are incorporated herein by reference.

Gene Expression Analysis by Massively Parallel Signature Sequencing (MPSS), described by Brenner et al., Nature Biotechnology 18:630 634 (2000) (which is incorporated herein by reference), is a sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microbeads. First, a microbead library of DNA templates is constructed by in vitro cloning. This is followed by the assembly of a planar array of the template-containing microbeads in a flow cell at a high density (typically greater than 3×10⁶ microbeads/cm²). The free ends of the cloned templates on each microbead are analyzed simultaneously, using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to simultaneously and accurately provide, in a single operation, hundreds of thousands of gene signature sequences from a yeast cDNA library.

E. Immunohistochemistry

Immunohistochemistry methods are also suitable for detecting the expression levels of the gene expression products of the informative genes described for use in the methods and compositions herein. Antibodies or antisera, preferably polyclonal antisera, and most preferably monoclonal antibodies, or other protein-binding ligands specific for each marker are used to detect expression. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as, biotin, or an enzyme such as horse radish peroxidase or alkaline phosphatase. Alternatively, unlabeled primary antibody is used in conjunction with a labeled secondary antibody, comprising antisera, polyclonal antisera or a monoclonal antibody specific for the primary antibody. Protocols and kits for immunohistochemical analyses are well known in the art and are commercially available.

III. COMPOSITIONS OF THE INVENTION

The methods for diagnosing lung cancer described herein which utilize defined gene expression profiles permit the development of simplified diagnostic tools for diagnosing lung cancer, e.g., NSCLC vs. non-cancerous nodule. Thus, a composition for diagnosing lung cancer in a mammalian subject as described herein can be a kit or a reagent. For example, one embodiment of a composition includes a substrate upon which said polynucleotides or oligonucleotides or ligands or ligands are immobilized. In another embodiment, the composition is a kit containing the relevant 5 or more polynucleotides or oligonucleotides or ligands, optional detectable labels for same, immobilization substrates, optional substrates for enzymatic labels, as well as other laboratory items. In still another embodiment, at least one polynucleotide or oligonucleotide or ligand is associated with a detectable label.

In one embodiment, a composition for diagnosing lung cancer in a mammalian subject includes 5 or more PCR primer-probe sets. Each primer-probe set amplifies a different polynucleotide sequence from a gene expression product of 5 or more informative genes found in the blood of the subject. These informative genes are selected to form a gene expression profile or signature which is distinguishable between a subject having lung cancer and a subject having a non-cancerous nodule. Changes in expression in the genes in the gene expression profile from that of a reference gene expression profile are correlated with a lung cancer, such as non-small cell lung cancer (NSCLC).

In one embodiment of this composition, the informative genes are selected from among the genes identified in Table I. In another embodiment of this composition, the informative genes are selected from among the genes identified in Table II. This collection of genes is those for which the gene product expression is altered (i.e., increased or decreased) versus the same gene product expression in the blood of a reference control (i.e., a patient having a non-cancerous nodule). In one embodiment, polynucleotide or oligonucleotide or ligands, i.e., probes, are generated to 5 or more informative genes from Table I or Table II for use in the composition (the CodeSet). An example of such a composition contains probes to a targeted portion of the 559 genes of Table I. In another embodiment, probes are generated to all 559 genes from Table I for use in the composition. In another embodiment, probes are generated to the first 539 genes from Table I for use in the composition. In another embodiment, probes are generated to the first 3 genes from Table I or Table II for use in the composition. In another embodiment, probes are generated to the first 5 genes from Table I or Table II for use in the composition. In another embodiment, probes are generated to the first 10 genes from Table I or Table II for use in the composition. In another embodiment, probes are generated to the first 15 genes from Table I or Table II for use in the composition. In another embodiment, probes are generated to the first 20 genes from Table I or Table II for use in the composition. In another embodiment, probes are generated to the first 25 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 30 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 35 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 40 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 45 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 50 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 60 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 65 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 70 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 75 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 80 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 85 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 90 genes from Table I or Table II for use in the composition. In yet another embodiment, probes are generated to the first 95 genes from Table I or Table II for use in the composition. In another embodiment, probes are generated to the first 100 genes from Table I or Table II for use in the composition. In another embodiment, probes are generated to the first 200 genes from Table I for use in the composition. In yet another embodiment, probes are generated to 300 genes from Table I for use in the composition. Still other embodiments employ probes to a targeted portion of other combinations of the genes in Table I or Table II. The selected genes from the Table need not be in rank order; rather any combination that clearly shows a difference in expression between the reference control to the diseased patient is useful in such a composition.

In one embodiment of the compositions described above, the reference control is a non-healthy control (NHC) as described above. In other embodiments, the reference control may be any class of controls as described above in “Definitions”.

The compositions based on the genes selected from Table I or Table II described herein, optionally associated with detectable labels, can be presented in the format of a microfluidics card, a chip or chamber, or a kit adapted for use with the Nanostring, PCR, RT-PCR or Q PCR techniques described above. In one aspect, such a format is a diagnostic assay using TAQMAN® Quantitative PCR low density arrays. In another aspect, such a format is a diagnostic assay using the Nanostring nCounter platform.

For use in the above-noted compositions the PCR primers and probes are preferably designed based upon intron sequences present in the gene(s) to be amplified selected from the gene expression profile. Exemplary target sequences are shown in Table III. The design of the primer and probe sequences is within the skill of the art once the particular gene target is selected. The particular methods selected for the primer and probe design and the particular primer and probe sequences are not limiting features of these compositions. A ready explanation of primer and probe design techniques available to those of skill in the art is summarized in U.S. Pat. No. 7,081,340, with reference to publically available tools such as DNA BLAST software, the Repeat Masker program (Baylor College of Medicine), Primer Express (Applied Biosystems); MGB assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers.

In general, optimal PCR primers and probes used in the compositions described herein are generally 17-30 bases in length, and contain about 20-80%, such as, for example, about 50-60% G+C bases. Melting temperatures of between 50 and 80° C., e.g. about 50 to 70° C. are typically preferred.

In another aspect, a composition for diagnosing lung cancer in a mammalian subject contains a plurality of polynucleotides immobilized on a substrate, wherein the plurality of genomic probes hybridize to 100 or more gene expression products of 100 or more informative genes selected from a gene expression profile in the blood of the subject, the gene expression profile comprising genes selected from Table I. In another embodiment, a composition for diagnosing lung cancer in a mammalian subject contains a plurality of polynucleotides immobilized on a substrate, wherein the plurality of genomic probes hybridize to 10 or more gene expression products of 10 or more informative genes selected from a gene expression profile in the blood of the subject, the gene expression profile comprising genes selected from Table I or Table II. This type of composition relies on recognition of the same gene profiles as described above for the Nanostring compositions but employs the techniques of a cDNA array. Hybridization of the immobilized polynucleotides in the composition to the gene expression products present in the blood of the patient subject is employed to quantitate the expression of the informative genes selected from among the genes identified in Tables I or Table II to generate a gene expression profile for the patient, which is then compared to that of a reference sample. As described above, depending upon the identification of the profile (i.e., that of genes of Table I or subsets thereof, that of genes of Table II or subsets thereof), this composition enables the diagnosis and prognosis of NSCLC lung cancers. Again, the selection of the polynucleotide sequences, their length and labels used in the composition are routine determinations made by one of skill in the art in view of the teachings of which genes can form the gene expression profiles suitable for the diagnosis and prognosis of lung cancers.

In yet another aspect, a composition or kit useful in the methods described herein contain a plurality of ligands that bind to 100 or more gene expression products of 100 or more informative genes selected from a gene expression profile in the blood of the subject. In another embodiment, a composition or kit useful in the methods described herein contain a plurality of ligands that bind to 10 or more gene expression products of 10 or more informative genes selected from a gene expression profile in the blood of the subject. The gene expression profile contains the genes of Table I or Table II, as described above for the other compositions. This composition enables detection of the proteins expressed by the genes in the indicated Tables. While preferably the ligands are antibodies to the proteins encoded by the genes in the profile, it would be evident to one of skill in the art that various forms of antibody, e.g., polyclonal, monoclonal, recombinant, chimeric, as well as fragments and components (e.g., CDRs, single chain variable regions, etc.) may be used in place of antibodies. Such ligands may be immobilized on suitable substrates for contact with the subject's blood and analyzed in a conventional fashion. In certain embodiments, the ligands are associated with detectable labels. These compositions also enable detection of changes in proteins encoded by the genes in the gene expression profile from those of a reference gene expression profile. Such changes correlate with lung cancer in a manner similar to that for the PCR and polynucleotide-containing compositions described above.

For all of the above forms of diagnostic/prognostic compositions, the gene expression profile can, in one embodiment, include at least the first 25 of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 10 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 15 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 20 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 30 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 40 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 50 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 60 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 70 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 80 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 90 or more of the informative genes of Table I or Table II. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include all 100 of the informative genes of Table II. In one embodiment, for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include at least the first 100 of the informative genes of Table I. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 200 or more of the informative genes of Table I. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 300 or more of the informative genes of Table I. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 400 or more of the informative genes of Table I. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 500 or more of the informative genes of Table I. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include 539 or more of the informative genes of Table I. In another embodiment for all of the above forms of diagnostic/prognostic compositions, the gene expression profile can include all 559 of the informative genes of Table I.

These compositions may be used to diagnose lung cancers, such as stage I or stage II NSCLC. Further these compositions are useful to provide a supplemental or original diagnosis in a subject having lung nodules of unknown etiology.

IV. DIAGNOSTIC METHODS OF THE INVENTION

All of the above-described compositions provide a variety of diagnostic tools which permit a blood-based, non-invasive assessment of disease status in a subject. Use of these compositions in diagnostic tests, which may be coupled with other screening tests, such as a chest X-ray or CT scan, increase diagnostic accuracy and/or direct additional testing.

Thus, in one aspect, a method is provided for diagnosing lung cancer in a mammalian subject. This method involves identifying a gene expression profile in the blood of a mammalian, preferably human, subject. In one embodiment, the gene expression profile includes 100 or more gene expression products of 100 or more informative genes having increased or decreased expression in lung cancer. The gene expression profiles are formed by selection of 100 or more informative genes from the genes of Table I. In another embodiment, the gene expression profile includes 10 or more gene expression products of 10 or more informative genes having increased or decreased expression in lung cancer. The gene expression profiles are formed by selection of 10 or more informative genes from the genes of Table I. In another embodiment, the gene expression profiles are formed by selection of 10 or more informative genes from the genes of Table II. In another embodiment, the gene expression profile includes 10 or more gene expression products of 5 or more informative genes having increased or decreased expression in lung cancer. The gene expression profiles are formed by selection of 5 or more informative genes from the genes of Table I. In another embodiment, the gene expression profiles are formed by selection of 5 or more informative genes from the genes of Table II. Comparison of a subject's gene expression profile with a reference gene expression profile permits identification of changes in expression of the informative genes that correlate with a lung cancer (e.g., NSCLC). This method may be performed using any of the compositions described above. In one embodiment, the method enables the diagnosis of a cancerous tumor from a benign nodule.

In another aspect, use of any of the compositions described herein is provided for diagnosing lung cancer in a subject.

The diagnostic compositions and methods described herein provide a variety of advantages over current diagnostic methods. Among such advantages are the following. As exemplified herein, subjects with cancerous tumors are distinguished from those with benign nodules. These methods and compositions provide a solution to the practical diagnostic problem of whether a patient who presents at a lung clinic with a small nodule has malignant disease. Patients with an intermediate-risk nodule would clearly benefit from a non-invasive test that would move the patient into either a very low-likelihood or a very high-likelihood category of disease risk. An accurate estimate of malignancy based on a genomic profile (i.e. estimating a given patient has a 90% probability of having cancer versus estimating the patient has only a 5% chance of having cancer) would result in fewer surgeries for benign disease, more early stage tumors removed at a curable stage, fewer follow-up CT scans, and reduction of the significant psychological costs of worrying about a nodule. The economic impact would also likely be significant, such as reducing the current estimated cost of additional health care associated with CT screening for lung cancer, i.e., $116,000 per quality adjusted life-year gained. A non-invasive blood genomics test that has a sufficient sensitivity and specificity would significantly alter the post-test probability of malignancy and thus, the subsequent clinical care.

A desirable advantage of these methods over existing methods is that they are able to characterize the disease state from a minimally-invasive procedure, i.e., by taking a blood sample. In contrast, current practice for classification of cancer tumors from gene expression profiles depends on a tissue sample, usually a sample from a tumor. In the case of very small tumors a biopsy is problematic and clearly if no tumor is known or visible, a sample from it is impossible. No purification of tumor is required, as is the case when tumor samples are analyzed. A recently published method depends on brushing epithelial cells from the lung during bronchoscopy, a method which is also considerably more invasive than taking a blood sample. Blood samples have an additional advantage, which is that the material is easily prepared and stabilized for later analysis, which is important when messenger RNA is to be analyzed.

The 559 classifier described herein showed a ROC-AUC of 0.81 over all tested samples. In one embodiment, when the sensitivity is about 90%, the specificity is about 46%. When the nodule classification accuracy is assessed by size without using a specific threshold for sensitivity, as nodules size and the cancer risk factor increases, the number of benign nodules classified as cancer increases. In one embodiment, the accuracy of the gene classifier is about 89% for nodules ≤8 mm. In another embodiment, the accuracy of the gene classifier is about 75% for nodules >8 to about ≤12 mm. In yet another embodiment, the accuracy of the gene classifier is about 68% for nodules >12 to about ≤16 mm. In another embodiment, the accuracy of the gene classifier is about 53% for ≥16 mm. See examples below.

In one embodiment, for nodules about <10 mm, the specificity is about 54% and the ROC-AUC to 0.85 at about 90% sensitivity. In another embodiment, for larger nodules, about >10 mm, the specificity is about 24% and the ROC-AUC about 0.71 at about 90% sensitivity.

The 100 Classifier described herein showed a ROC-AUC of 0.82 over all tested samples. In one embodiment, when the sensitivity is about 90%, the specificity is about 62%. In another embodiment, when the sensitivity is about 79%, the specificity is about 68%. In one embodiment, when the sensitivity is about 71%, the specificity is about 75%. See examples below.

These compositions and methods allow for more accurate diagnosis and treatment of lung cancer. Thus, in one embodiment, the methods described include treatment of the lung cancer. Treatment may removal of the neoplastic growth, chemotherapy and/or any other treatment known in the art or described herein.

In one embodiment, a method for diagnosing the existence or evaluating a lung cancer in a mammalian subject is provided, which includes identifying changes in the expression of 5, 10, 15 or more genes in the sample of said subject, said genes selected from the genes of Table I or the genes of Table II. The subject's gene expression levels are compare with the levels of the same genes in a reference or control, wherein changes in expression of the subject's genes from those of the reference correlates with a diagnosis or evaluation of a lung cancer.

In one embodiment, the diagnosis or evaluation comprise one or more of a diagnosis of a lung cancer, a diagnosis of a benign nodule, a diagnosis of a stage of lung cancer, a diagnosis of a type or classification of a lung cancer, a diagnosis or detection of a recurrence of a lung cancer, a diagnosis or detection of a regression of a lung cancer, a prognosis of a lung cancer, or an evaluation of the response of a lung cancer to a surgical or non-surgical therapy. In another embodiment, the changes comprise an upregulation of one or more selected genes in comparison to said reference or control or a downregulation of one or more selected genes in comparison to said reference or control.

In one embodiment, the method includes the size of a lung nodule in the subject. The specificity and sensitivity may be variable based on the size of the nodule. In one embodiment, the specificity is about 46% at about 90% sensitivity. In another embodiment, the specificity is about 54% at about 90% sensitivity for nodules <10 mm. In yet another embodiment, the accuracy is about 88% for nodules ≤8 mm, about 75% for nodules >8 mm and ≤12 mm, about 68% for nodules >12 mm and ≤16 mm, and about 53% for nodules >16 mm.

In another embodiment, the reference or control comprises three or more genes of Table I sample of at least one reference subject. The reference subject may be selected from the group consisting of: (a) a smoker with malignant disease, (b) a smoker with non-malignant disease, (c) a former smoker with non-malignant disease, (d) a healthy non-smoker with no disease, (e) a non-smoker who has chronic obstructive pulmonary disease (COPD), (f) a former smoker with COPD, (g) a subject with a solid lung tumor prior to surgery for removal of same; (h) a subject with a solid lung tumor following surgical removal of said tumor; (i) a subject with a solid lung tumor prior to therapy for same; and (j) a subject with a solid lung tumor during or following therapy for same. In one embodiment, the reference or control subject (a)-(j) is the same test subject at a temporally earlier timepoint.

The sample is selected from those described herein. In one embodiment, the sample is peripheral blood. The nucleic acids in the sample are, in some embodiments, stabilized prior to identifying changes in the gene expression levels. Such stabilization may be accomplished, e.g., using the Pax Gene system, described herein.

In one embodiment, the method of detecting lung cancer in a patient includes

-   -   a. obtaining a sample from the patient; and     -   b. detecting a change in expression in at least 10 genes         selected from Table I or Table II in the patient sample as         compared to a control by contacting the sample with a         composition comprising oligonucleotides, polynucleotides or         ligands specific for each different gene transcript or         expression product of the at least 10 gene of Table I or Table         II and detecting binding between the oligonucleotide,         polynucleotide or ligand and the gene product or expression         product.

In another embodiment, the method of diagnosing lung cancer in a subject includes

-   -   a. obtaining a blood sample from a subject;     -   b. detecting a change in expression in at least 10 genes         selected from Table I or Table II in the patient sample as         compared to a control by contacting the sample with a         composition comprising oligonucleotides, polynucleotides or         ligands specific for each different gene transcript or         expression product of the at least 100 gene of Table I or Table         II and detecting binding between the oligonucleotide,         polynucleotide or ligand and the gene product or expression         product; and     -   c. diagnosing the subject with cancer when changes in expression         of the subject's genes from those of the reference are detected.

In yet another embodiment, the method includes

-   -   a. obtaining a blood sample from a subject;     -   b. detecting a change in expression in at least 10 genes         selected from Table I or Table II in the patient sample as         compared to a control by contacting the sample with a         composition comprising oligonucleotides, polynucleotides or         ligands specific for each different gene transcript or         expression product of the at least 10 genes of Table I or Table         II and detecting binding between the oligonucleotide,         polynucleotide or ligand and the gene product or expression         product;     -   c. diagnosing the subject with cancer when changes in expression         of the subject's genes from those of the reference are detected;         and     -   d. removing the neoplastic growth.

V. EXAMPLES

The invention is now described with reference to the following examples. These examples are provided for the purpose of illustration only and the invention should in no way be construed as being limited to these examples but rather should be construed to encompass any and all variations that become evident as a result of the teaching provided herein.

Example 1: Patient Population—Analysis A

For development of the gene classifier described herein, blood samples and clinical information were collected from 150 subjects, 73 having a diagnosis of lung cancer and 77 having a diagnosis of benign nodule. Patient characteristics are shown in FIG. 1 .

Patients with lung cancer included newly diagnosed male and female patients with early stage lung cancer. They were in moderately good health (ambulatory), although with medical illness. They were excluded if they have had previous cancers, chemotherapy, radiation, or cancer surgery. They must have had a lung cancer diagnosis within preceding 6 months, histologic confirmation, and no systemic therapy, such as chemotherapy, radiation therapy or cancer surgery as biomarker levels may change with therapy. Thus the majority of the cancer patients were early stage (i.e., Stage I and Stage II).

The “control” cohort was derived from patients with benign lung nodules (e.g. ground glass opacities, single nodules, granulomas or hamartomas). These patients were evaluated at pulmonary clinics, or underwent thoracic surgery for a lung nodule. All samples were collected prior to surgery.

Example 2: Patient Population—Analysis B

Further blood samples and clinical information were collected from 120 subjects, 60 having a diagnosis of lung cancer and 60 having a diagnosis of benign nodule. Patients with lung cancer included newly diagnosed male and female patients with early stage lung cancer. They were in moderately good health (ambulatory), although with medical illness. They were excluded if they have had previous cancers, chemotherapy, radiation, or cancer surgery. They must have had a lung cancer diagnosis within preceding 6 months, histologic confirmation, and no systemic therapy, such as chemotherapy, radiation therapy or cancer surgery as biomarker levels may change with therapy. Thus the majority of the cancer patients were early stage (i.e., Stage I and Stage II).

The “control” cohort was derived from patients with benign lung nodules (e.g. granulomas or hamartomas). These patients were evaluated at pulmonary clinics, or underwent thoracic surgery for a lung nodule. All samples were collected prior to surgery.

Example 3: Sample Collection Protocols and Processing

Blood samples were collected in the clinic by the tissue acquisition technician. Blood samples were drawn directly into PAXgene Blood RNA Tubes via standard phlebotomy technique. These tubes contain a proprietary reagent that immediately stabilizes intracellular RNA, minimizing the ex-vivo degradation or up-regulation of RNA transcripts. The ability to eliminate freezing, batch samples, and to minimize the urgency to process samples following collection, greatly enhances lab efficiency and reduces costs.

Example 4—RNA Purification and Quality Assessment

PAXgene RNA is prepared using a standard commercially available kit from Qiagen™ that allows purification of mRNA. The resulting RNA is used for mRNA profiling. The RNA quality is determined using a Bioanalyzer. Only samples with RNA Integrity numbers >3 were used.

Briefly, RNA is isolated as follows. Turn shaker-incubator on and set to 55° C. before beginning. Unless otherwise noted, all steps in this protocol including centrifugation steps, should be carried out at room temp (15-25° C.). This protocol assumes samples are stores at −80° C. Unfrozen samples that have been left a RT per the Qiagen protocol of a minimum of 2 hours should be processed in the same way.

Thaw Paxgene tubes upright in a plastic rack. Invert tubes at least 10 times to mix before starting isolation. Prepare all necessary tubes. For each sample, the following are needed: 2 numbered 1.5 ml Eppendorf tubes; 1 Eppendorf tube with the sample information (this is the final tube); 1 Lilac Paxgene spin column; 1 Red Paxgene Spin column; and 5 Processing tubes.

Centrifuge the PAXgene Blood RNA Tube for 10 minutes at 5000×g using a swing-out rotor in Qiagen centrifuge. (Sigma 4-15° C. Centrifuge., Rotor: Sigma Nr. 11140, 7/01, 5500/min, Holder: Sigma 13115,286 g 14/D, Inside tube holder: 18010, 125 g). Note: After thawed, ensure that the blood sample has been incubated in the PAXgene Blood RNA Tube for a minimum of 2 hours at room temperature (15-25° C.), in order to achieve complete lysis of blood cells.

Under the hood—remove the supernatant by decanting into bleach. When the supernatant is decanted, take care not to disturb the pellet, and dry the rim of the tube with a clean paper towel. Discard the decanted supernatant by placing the clotted blood into a bag and then into the infectious waste and discard the fluid portion down the sink and wash down with a lot of water. Add 4 ml RNase-free water to the pellet, and close the tube using a fresh secondary Hemogard closure.

Vortex until the pellet is visibly dissolved. Weigh the tubes in the centrifuge holder again to ensure they are balanced, and centrifuge for 10 minutes at 5000×g using a swing-out rotor Qiagen centrifuge. Small debris remaining in the supernatant after vortexing but before centrifugation will not affect the procedure.

Remove and discard the entire supernatant. Leave tube upside-down for 1 min to drain off all supernatant. Incomplete removal of the supernatant will inhibit lysis and dilute the lysate, and therefore affect the conditions for binding RNA to the PAXgene membrane.

Add 350 μl Buffer BM1 and pipet up and down lyse the pellet.

Pipet the re-suspended sample into a labeled 1.5 ml microcentrifuge tube. Add 300 μl Buffer BM2. Then add 40 μl proteinase K. Mix by vortexing for 5 seconds, and incubate for 10 minutes at 55° C. using a shaker-incubator at the highest possible speed, 800 rpm on Eppendorf thermomixer. (If using a shaking water bath instead of a thermomixer, quickly vortex the samples every 2-3 minutes during the incubation. Keep the vortexer next to the incubator).

Pipet the lysate directly into a PAXgene Shredder spin column (lilac tube) placed in a 2 ml processing tube, and centrifuge for 3 minutes at 24 C at 18,500×g in the TOMY Microtwin centrifuge. Carefully pipet the lysate into the spin column and visually check that the lysate is completely transferred to the spin column. To prevent damage to columns and tubes, do not exceed 20,000×g.

Carefully transfer the entire supernatant of the flow-through fraction to a fresh 1.5 ml microcentrifuge tube without disturbing the pellet in the processing tube. Discard the pellet in the processing tube.

Add 700 μl isopropanol (100%) to the supernatant. Mix by vortexing.

Pipet 690 μl sample into the PAXgene RNA spin column (red) placed in a 2 ml processing tube, and centrifuge for 1 minute at 10,000×g. Place the spin column in a new 2 ml processing tube, and discard the old processing tube containing flow-through.

Pipet the remaining sample into the PAXgene RNA spin column (red), and centrifuge for 1 minute at 18,500×g. Place the spin column in a new 2 ml processing tube, and discard the old processing tube containing flow-through. Carefully pipet the sample into the spin column and visually check that the sample is completely transferred to the spin column.

Pipet 350 μl Buffer BM3 into the PAXgene RNA spin column. Centrifuge for 15 sec at 10,000×g. Place the spin column in a new 2 ml processing tube, and discard the old processing tube containing flow-through.

Prepare DNase I incubation mix for step 13. Add 10 μl DNase I stock solution to 70 μl Buffer RDD in a 1.5 ml microcentrifuge tube. Mix by gently flicking the tube, and centrifuge briefly to collect residual liquid from the sides of the tube.

Pipet the DNase I incubation mix (80 l) directly onto the PAXgene RNA spin column membrane, and place on the benchtop (20-30° C.) for 15 minutes. Ensure that the DNase I incubation mix is placed directly onto the membrane. DNase digestion will be incomplete if part of the mix is applied to and remains on the walls or the O-ring of the spin column.

Pipet 350 μl Buffer BM3 into the PAXgene RNA spin column, and centrifuge for 15 sec at 18,500×g. Place the spin column in a new 2 ml processing tube, and discard the old processing tube containing flow-through.

Pipet 500 μl Buffer BM4 to the PAXgene RNA spin column, and centrifuge for 15 sec at 10,000×g. Place the spin column in a new 2 ml processing tube, and discard the old processing tube containing flow-through.

Add another 500 μl Buffer BM4 to the PAXgene RNA spin column. Centrifuge for 2 minutes at 18,500×g.

Discard the tube containing the flow-through, and place the PAXgene RNA spin column in a new 2 ml processing tube. Centrifuge for 1 minute at 18,500×g.

Discard the tube containing the flow-through. Place the PAXgene RNA spin column in a labeled 1.5 ml microcentrifuge tube (final tube), and pipet 40 μl Buffer BR5 directly onto the PAXgene RNA spin column membrane. Centrifuge for 1 minute at 10,000×g to elute the RNA. It is important to wet the entire membrane with Buffer BR5 in order to achieve maximum elution efficiency.

Repeat the elution step as described, using 40 μl Buffer BR5 and the same microcentrifuge tube. Centrifuge for 1 minute at 20,000×g to elute the RNA.

Incubate the eluate for 5 minutes at 65° C. in the shaker-incubator without shaking. After incubation, chill immediately on ice. This incubation at 65° C. denatures the RNA for downstream applications. Do not exceed the incubation time or temperature.

If the RNA samples will not be used immediately, store at −20° C. or −70° C. Since the RNA remains denatured after repeated freezing and thawing, it is not necessary to repeat the incubation at 65° C.

Example 5: Measurement of RNA Levels

To provide a biomarker signature that can be used in clinical practice to diagnose lung cancer, a gene expression profile with the smallest number of genes that maintain satisfactory accuracy is provided by the use of 100 more of the genes identified in Table I as well as by the use of 10 or more of the genes identified in Table II. These gene profiles or signatures permit simpler and more practical tests that are easy to use in a standard clinical laboratory. Because the number of discriminating genes is small enough, NanoString nCounter® platforms are developed using these gene expression profiles.

A. Nanostring nCounter® Platform Gene Expression Assay Protocol

Total RNA was isolated from whole blood using the Paxgene Blood miRNA Kit, as described above, and samples were checked for RNA quality. Samples were analyzed with the Agilent 2100 Bioanalyzer on a RNA Nano chip, using the RIN score and electropherogram picture as indicators for good sample integrity. Samples were also quantitated on the Nanodrop (ND-1000 Spectrophotometer) where 260/280 and 260/230 readings were recorded and evaluated for Nanostring-compatibility. From the concentrations taken by Nanodrop, total RNA samples were normalized to contain 100 ng in 5 μL, using Nuclease-free water as diluent, into Nanostring-provided tube strips. An 8 μL aliquot of a mixture of the Nanostring nCounter Reporter CodeSet and Hybridization Buffer (70 μL Hybridization Buffer, 42 μL Reporter CodeSet per 12 assays) and 2 μL of Capture ProbeSet was added to each 5 μL RNA sample. Samples were hybridized for 19 hours at 65° C. in the Thermocycler (Eppendorf). During hybridization, Reporter Probes, which have fluorescent barcodes specific to each mRNA of interest to the user, and biotinylated Capture Probes bound to their associated target mRNA to create target-probe complexes. After hybridization was complete, samples were then transferred to the nCounter Prep Station for processing using the Standard Protocol setting (Run Time: 2 hr 35 min). The Prep Station robot, during the Standard Protocol, washed samples to remove excess Reporter and Capture Probes. Samples were moved to a streptavidin-coated cartridge where purified target-probe complexes were immobilized in preparation for imaging by the nCounter Digital Analyzer. Upon completion, the cartridge was sealed and placed in the Digital Analyzer using a Field of View (FOV) setting at 555. A fluorescent microscope tabulated the raw counts for each unique barcode associated with a target mRNA. Data collected was stored in .csv files and then transferred to the Bioinformatics Facility for analysis according to the manufacturer's instructions.

Example 6: Biomarker Selection

Support Vector Machine (SVM) can be applied to gene expression datasets for gene function discovery and classification. SVM has been found to be most efficient at distinguishing the more closely related cases and controls that reside in the margins. Primarily SVM-RFE (48, 54) was used to develop gene expression classifiers which distinguish clinically defined classes of patients from clinically defined classes of controls (smokers, non-smokers, COPD, granuloma, etc). SVM-RFE is a SVM based model utilized in the art that removes genes, recursively based on their contribution to the discrimination, between the two classes being analyzed. The lowest scoring genes by coefficient weights were removed and the remaining genes were scored again and the procedure was repeated until only a few genes remained. This method has been used in several studies to perform classification and gene selection tasks. However, choosing appropriate values of the algorithm parameters (penalty parameter, kernel-function, etc.) can often influence performance.

SVM-RCE is a related SVM based model, in that it, like SVM-RFE assesses the relative contributions of the genes to the classifier. SVM-RCE assesses the contributions of groups of correlated genes instead of individual genes. Additionally, although both methods remove the least important genes at each step, SVM-RCE scores and removes clusters of genes, while SVM-RFE scores and removes a single or small numbers of genes at each round of the algorithm.

The SVM-RCE method is briefly described here. Low expressing genes (average expression less than 2× background) were removed, quantile normalization performed, and then “outlier” arrays whose median expression values differ by more than 3 sigma from the median of the dataset were removed. The remaining samples were subject to SVM-RCE using ten repetitions of 10-fold cross-validation of the algorithm. The genes were reduced by t-test (applied on the training set) to an experimentally determined optimal value which produces highest accuracy in the final result. These starting genes were clustered by K-means into clusters of correlated genes whose average size is 3-5 genes. SVM classification scoring was carried out on each cluster using 3-fold resampling repeated 5 times, and the worst scoring clusters eliminated. Accuracy is determined on the surviving pool of genes using the left-out 10% of samples (testing set) and the top-scoring 100 genes were recorded. The procedure was repeated from the clustering step to an end point of 2 clusters. The optimal gene panel was taken to be the minimal number of genes which gives the maximal accuracy starting with the most frequently selected gene. The identity of the individual genes in this panel is not fixed, since the order reflects the number of times a given gene was selected in the top 100 informative genes and this order is subject to some variation.

A. Biomarker Selection.

Genes which score highest (by SVM) in discriminating cancerous tumors from benign nodules were examined for their utility for clinical tests. Factors considered include, higher differences in expression levels between classes, and low variability within classes. When selecting biomarkers for validation an effort was made to select genes with distinct expression profiles to avoid selection of correlated genes and to identify genes with differential expression levels that were robust by alternative techniques including PCR and/or immuno-histochemistry.

B. Validation.

Three methods of validation were considered.

Cross-Validation: To minimize over-fitting within a dataset, K-fold cross-validation (K usually equal to 10) was used, when the dataset is split on K parts randomly and K−1 parts were used for training and 1 for testing. Thus, for K=10 the algorithm was trained on a random selection of 90% of the patients and 90% of the controls and then tested on the remaining 10%. This was repeated until all of the samples have been employed as test subjects and the cumulated classifier makes use of all of the samples, but no sample is tested using a training set of which it is a part. To reduce the randomization impact, K-fold separation was performed M times producing different combinations of patients and controls in each of K folds each time. Therefore, for individual dataset M*K rounds of permuted selection of training and testing sets were used for each set of genes.

Independent Validation: To estimate the reproducibility of the data and the generality of the classifier, one needs to examine the classifier that was built using one dataset and tested using another dataset to estimate the performance of the classifier. To estimate the performance, validation on the second set was performed using the classifier developed with the original dataset.

Resampling (permutation): To demonstrate dependence of the classifier on the disease state, patients and controls from the dataset were chosen at random (permuted) and the classification was repeated. The accuracy of classification using randomized samples was compared to the accuracy of the developed classifier to determine the p value for the classifier, i.e., the possibility that the classifier might have been chosen by chance. In order to test the generality of a classifier developed in this manner, it was used to classify independent sets of samples that were not used in developing the classifier. The cross-validation accuracies of the permuted and original classifier were compared on independent test sets to confirm its validity in classifying new samples.

C. Classifier Performance

Performance of each classifier was estimated by different methods and several performance measurements were used for comparing classifiers between each other. These measurements include accuracy, area under ROC curve, sensitivity, specificity, true positive rate and true negative rate. Based on the required properties of the classification of interest, different performance measurements can be used to pick the optimal classifier, e.g. classifier to use in screening of the whole population would require better specificity to compensate for small (˜1%) prevalence of the disease and therefore avoid large number of false positive hits, while a diagnostic classifier of patients in hospital should be more sensitive.

For diagnosing cancerous tumors from benign nodules, higher sensitivity is more desirable than specificity, as the patients are already at high risk.

Example 7: Testing of the Classifiers

Peripheral blood samples were all collected in PAXgene RNA stabilizations tubes and RNA was extracted according to the manufacturer. Samples were tested on a Nanostring nCounter™ (as described above) against a custom panel of 559 probes (Table III). In addition, they were tested against a 100 probe subset of 559 marker panel.

For the 559 Classifier, 432 were selected based on previous microarray data, 107 probes were selected from Nanostring studies and 20 were housekeeping genes. We analyzed 610 PAXgene RNA samples (278 cancers, 332 controls) derived from 5 collection sites. For QC, a Universal RNA standard (Agilent) was included in each batch of 36 samples tested. Probe expression values were normalized using the 20 housekeeping genes as well as spike-in positive and negative controls supplied by Nanostring (included in classifier). Zscores were calculated for probe count values and served as the input to a Support Vector Machine (SVM) classifier using a polynomial kernel. Classification performance was evaluated by 10-fold cross-validation of the samples.

A. 559 Classifier

As shown in FIGS. 2A to 2B, the 559 classifier developed on all the samples showed a ROC-AUC of 0.81 (FIG. 2A). With the Sensitivity set at 90%, the specificity is 46%. When performed on a balanced set of 556 samples (278 cancer, 278 nodule), similar performance is shown (FIG. 2B). For both sets, UHR controls, post samples, and patients with other cancers were excluded.

When nodule classification accuracy is assessed by size without using a specific threshold for sensitivity, we find that as nodules size and the cancer risk factor increases, the number of benign nodules classified as cancer increases. FIG. 3 . In this analysis, nodules ≤8 mm were correctly classified 88.9% of the time, for nodules >8, ≤12 mm accuracy was 75%, for nodules >12, ≤16 mm accuracy was 68%, for nodules >16 mm accuracy is 53.6%. See Table IV below.

TABLE IV Nodule Size Correct Incorrect Total Specificity <=5 mm 108 19 127 85.0% >5, <=8 mm 88 11 99 88.9%  >8, <=12 mm 40 13 53 75.5% >12, <=16 mm 17 8 25 68.0% >16 mm 15 13 28 53.6% Total 268 64 332 80.7%

A second set of nodules was tested and the accuracy of the classifier for size groups was determined by sample group (cancer vs benign nodule). Similarly, as nodule size and the cancer risk factor increases, the number of benign nodules classified as cancer increases (FIGS. 4A to 4C). For cancers >5 mm and higher, r=0.95. For nodules of all sizes, r=0.97. The chart shows the sensitivity and specificity of the classification of cancers and nodules based on lesion size. These numbers are shown in bar graph form below.

Since classification accuracy was found to be negatively correlated with benign nodule size, we reanalyzed the data using only nodules <10 mm (n=244) (FIG. 5A) and sensitivity fixed at 90%, in this case the specificity rises to 54% and the ROC-AUC to 0.85. For larger nodules, >10 mm (n=88) the specificity drops to 24% and the ROC-AUC drops to 0.71 (FIG. 5B). See Table V below.

TABLE V Small Large ≤10 mm >10 mm All nodules N (nodules) 244 88 332 min 1 10.4 1 max 10 90 90 mean 6.07 17.8 8.7 median 6 15 6 std 1.73 10.6 7.13 ROC Area 0.85 0.71 0.81 Specificity at 54% 42% 46% 90% Sensitivity

B. 100 Marker Classifier

We now reanalyzed the data from the 633 samples analyzed by W559 on the Nanostring platform in order to identify the minimal number of probes required to maintain performance attained with the whole panel. We used SVM-RFE for probe selection as previously described. We used 75% of the data for the training set with SVM-RFE and the tested the performance of top 100 probes (Table II) selected by this process on an independent testing set composed of 25% of the samples. Samples were randomly selected for training and testing sets Table VI below. The accuracy obtained on the testing set is shown in FIG. 6 . In this analysis, at a sensitivity of 90%, specificity was 62%; at a sensitivity of 79%, specificity was 68%; and at a sensitivity of 71%, specificity was 75% (FIG. 6 ). In summary the ROC-AUC is 0.82 and at a sensitivity of 0.90 we achieve a specificity of 0.62.

TABLE VI nodules cancer > <= n > <= n 0 5 130 0 14 86 5 8 109 14 22 75 8 12.5 65 22 33 64 12.5 57 33 47

Each and every patent, patent application, and publication, including the priority application, U.S. Provisional Patent Application No. 62/352,865, filed Jun. 21, 2016, and publically available gene sequence cited throughout the disclosure is expressly incorporated herein by reference in its entirety. While this invention has been disclosed with reference to specific embodiments, it is apparent that other embodiments and variations of this invention are devised by others skilled in the art without departing from the true spirit and scope of the invention. The appended claims include such embodiments and equivalent variations. 

1. A composition for diagnosing the existence or evaluating the progression of a lung cancer in a mammalian subject, said composition comprising at least 10 polynucleotides or oligonucleotides, wherein each polynucleotide or oligonucleotide hybridizes to a different gene, gene fragment, gene transcript or expression product in a sample selected from the genes of Table I.
 2. The composition of claim 1, wherein at least one polynucleotide or oligonucleotide is attached to a detectable label.
 3. The composition of claim 2, wherein each polynucleotide or oligonucleotide is attached to a different detectable label.
 4. The composition of claim 1, further comprising a capture oligonucleotide, which hybridizes to at least one polynucleotide or oligonucleotide.
 5. The composition of claim 4, wherein the capture oligonucleotide is capable of hybridizing to each polynucleotide or oligonucleotide.
 6. The composition of claim 4, wherein the capture oligonucleotide binds to a substrate.
 7. The composition of claim 6, further comprising a substrate to which the capture oligonucleotide binds.
 8. The composition of claim 1, comprising at least 15 polynucleotides or oligonucleotides.
 9. The composition of claim 1, comprising at least 25 polynucleotides or oligonucleotides.
 10. The composition of claim 1, comprising at least 50 polynucleotides or oligonucleotides.
 11. The composition of claim 1, comprising at least 100 polynucleotides or oligonucleotides.
 12. The composition of claim 1, comprising at least 500 polynucleotides or oligonucleotides.
 13. The composition of claim 1, comprising polynucleotides or oligonucleotides capable of hybridizing to each different gene, gene fragment, gene transcript or expression product listed in Table I.
 14. A kit comprising the composition of claim 1 and an apparatus for sample collection.
 15. A method for diagnosing the existence or evaluating a lung cancer in a mammalian subject comprising identifying changes in the expression of 10 or more genes in the sample of said subject, said genes selected from the genes of Table I; and comparing said subject's gene expression levels with the levels of the same genes in a reference or control, wherein changes in expression of the subject's genes from those of the reference correlates with a diagnosis or evaluation of a lung cancer.
 16. The method according to claim 15, wherein said diagnosis or evaluation comprise one or more of a diagnosis of a lung cancer, a diagnosis of a benign nodule, a diagnosis of a stage of lung cancer, a diagnosis of a type or classification of a lung cancer, a diagnosis or detection of a recurrence of a lung cancer, a diagnosis or detection of a regression of a lung cancer, a prognosis of a lung cancer, or an evaluation of the response of a lung cancer to a surgical or non-surgical therapy.
 17. The method according to claim 15, wherein said changes comprise an upregulation of one or more selected genes in comparison to said reference or control or a downregulation of one or more selected genes in comparison to said reference or control.
 18. The method according to claim 15, further comprising identifying the size of a lung nodule in the subject.
 19. The method according to claim 15, wherein the specificity is about 46% at about 90% sensitivity or about 54% at about 90% for nodules <10 mm.
 20. The method according to claim 15, wherein the accuracy is about 88% for nodules ≤8 mm, about 75% for nodules ≥8 mm and <12 mm, about 68% for nodules >12 mm and ≤16 mm, and about 53% for nodules >16 mm. 